import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_log_error
df=pd.read_csv(r"C:\Users\prabha\Desktop\Dataset\energy-pop-exposure-nuclear-plants-locations\energy-pop-exposure-nuclear-plants-locations_plants.csv")
df
| FID | Region | Country | Plant | NumReactor | Latitude | Longitude | p90_1200 | p00_1200 | p10_1200 | ... | p10r_600 | p90_300 | p00_300 | p10_300 | p90u_300 | p00u_300 | p10u_300 | p90r_300 | p00r_300 | p10r_300 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Europe - Western | SWEDEN | AGESTA | 1 | 59.206022 | 18.082872 | 187382000 | 188684000 | 188250000 | ... | 8972550.0 | 5013240.0 | 5227700.0 | 5471110.0 | 3426880.0 | 3596030.0 | 3764920.0 | 1586350.0 | 1631670.0 | 1706190 |
| 1 | 1 | Europe - Western | SPAIN | ALMARAZ | 2 | 39.808100 | -5.696940 | 136675000 | 147718000 | 163429000 | ... | 19453700.0 | 17756500.0 | 18187800.0 | 20185200.0 | 10986000.0 | 11415400.0 | 12689800.0 | 6770480.0 | 6772380.0 | 7495340 |
| 2 | 2 | America - Latin | BRAZIL | ANGRA | 3 | -23.007857 | -44.458098 | 99195200 | 113894000 | 127898000 | ... | 18605600.0 | 39546400.0 | 44701700.0 | 50210600.0 | 32788500.0 | 37064600.0 | 41648300.0 | 6757940.0 | 7637110.0 | 8562300 |
| 3 | 3 | America - Northern | UNITED STATES OF AMERICA | ARKANSAS ONE | 2 | 35.310320 | -93.231289 | 117830000 | 132729000 | 146482000 | ... | 9498240.0 | 5603180.0 | 6226360.0 | 6866840.0 | 3779400.0 | 4198920.0 | 4633770.0 | 1823770.0 | 2027450.0 | 2233070 |
| 4 | 4 | Europe - Western | SPAIN | ASCO | 2 | 41.200000 | 0.566670 | 271854000 | 287134000 | 308922000 | ... | 17594700.0 | 14398500.0 | 15095600.0 | 16830700.0 | 11215400.0 | 11773600.0 | 13151300.0 | 3183180.0 | 3322050.0 | 3679470 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 271 | 271 | Asia - Far East | CHINA | YANGJIANG | 3 | 21.708333 | 112.261111 | 557032000 | 629419000 | 679747000 | ... | 86375000.0 | 68115100.0 | 87136800.0 | 93428600.0 | 41415700.0 | 53433600.0 | 57372000.0 | 26699400.0 | 33703200.0 | 36056500 |
| 272 | 272 | America - Northern | UNITED STATES OF AMERICA | YANKEE | 1 | 42.727839 | -72.929108 | 123430000 | 131527000 | 145162000 | ... | 9951640.0 | 35873400.0 | 37247000.0 | 41124300.0 | 32253500.0 | 33508100.0 | 37010000.0 | 3619890.0 | 3738910.0 | 4114260 |
| 273 | 273 | Asia - Far East | KOREA, REPUBLIC OF | YONGGWANG | 6 | 35.411130 | 126.416190 | 649425000 | 710033000 | 750775000 | ... | 35715500.0 | 42866100.0 | 46496100.0 | 48569700.0 | 36191700.0 | 39809100.0 | 41589500.0 | 6674350.0 | 6686990.0 | 6980200 |
| 274 | 274 | Europe - Central and Eastern | UKRAINE | ZAPOROZHE | 6 | 47.511809 | 34.585460 | 273432000 | 274834000 | 272561000 | ... | 22931000.0 | 20624400.0 | 19361900.0 | 17990600.0 | 13838800.0 | 12978800.0 | 12067100.0 | 6785590.0 | 6383160.0 | 5923510 |
| 275 | 275 | America - Northern | UNITED STATES OF AMERICA | ZION | 2 | 42.446428 | -87.803003 | 162525000 | 176001000 | 194245000 | ... | 11318900.0 | 19702800.0 | 21460200.0 | 23696900.0 | 16653600.0 | 18285600.0 | 20199900.0 | 3049180.0 | 3174560.0 | 3497080 |
276 rows × 61 columns
missing_values = df.isnull()
missing_count = missing_values.sum()
missing_percentage = (missing_count / len(df)) * 100
print("Missing and Null Values:\n", missing_count)
print("Missing and Null Values (%):\n", missing_percentage)
Missing and Null Values:
FID 0
Region 3
Country 0
Plant 0
NumReactor 0
..
p00u_300 0
p10u_300 0
p90r_300 0
p00r_300 0
p10r_300 0
Length: 61, dtype: int64
Missing and Null Values (%):
FID 0.000000
Region 1.086957
Country 0.000000
Plant 0.000000
NumReactor 0.000000
...
p00u_300 0.000000
p10u_300 0.000000
p90r_300 0.000000
p00r_300 0.000000
p10r_300 0.000000
Length: 61, dtype: float64
df.dropna(axis='columns', inplace=True)
columns_to_drop = ['FID', 'Plant', 'NumReactor']
df.drop(columns=columns_to_drop, inplace=True)
df
| Country | Latitude | Longitude | p90_1200 | p00_1200 | p10_1200 | p90u_1200 | p00u_1200 | p10u_1200 | p90r_1200 | ... | p10r_600 | p90_300 | p00_300 | p10_300 | p90u_300 | p00u_300 | p10u_300 | p90r_300 | p00r_300 | p10r_300 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | SWEDEN | 59.206022 | 18.082872 | 187382000 | 188684000 | 188250000 | 121178000.0 | 122299000.0 | 122399000.0 | 66203700 | ... | 8972550.0 | 5013240.0 | 5227700.0 | 5471110.0 | 3426880.0 | 3596030.0 | 3764920.0 | 1586350.0 | 1631670.0 | 1706190 |
| 1 | SPAIN | 39.808100 | -5.696940 | 136675000 | 147718000 | 163429000 | 87539700.0 | 93943200.0 | 103704000.0 | 49135800 | ... | 19453700.0 | 17756500.0 | 18187800.0 | 20185200.0 | 10986000.0 | 11415400.0 | 12689800.0 | 6770480.0 | 6772380.0 | 7495340 |
| 2 | BRAZIL | -23.007857 | -44.458098 | 99195200 | 113894000 | 127898000 | 67751500.0 | 77821400.0 | 87432200.0 | 31443800 | ... | 18605600.0 | 39546400.0 | 44701700.0 | 50210600.0 | 32788500.0 | 37064600.0 | 41648300.0 | 6757940.0 | 7637110.0 | 8562300 |
| 3 | UNITED STATES OF AMERICA | 35.310320 | -93.231289 | 117830000 | 132729000 | 146482000 | 92609500.0 | 104781000.0 | 115697000.0 | 25220200 | ... | 9498240.0 | 5603180.0 | 6226360.0 | 6866840.0 | 3779400.0 | 4198920.0 | 4633770.0 | 1823770.0 | 2027450.0 | 2233070 |
| 4 | SPAIN | 41.200000 | 0.566670 | 271854000 | 287134000 | 308922000 | 190825000.0 | 200465000.0 | 215092000.0 | 81029500 | ... | 17594700.0 | 14398500.0 | 15095600.0 | 16830700.0 | 11215400.0 | 11773600.0 | 13151300.0 | 3183180.0 | 3322050.0 | 3679470 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 271 | CHINA | 21.708333 | 112.261111 | 557032000 | 629419000 | 679747000 | 250848000.0 | 287891000.0 | 309439000.0 | 306184000 | ... | 86375000.0 | 68115100.0 | 87136800.0 | 93428600.0 | 41415700.0 | 53433600.0 | 57372000.0 | 26699400.0 | 33703200.0 | 36056500 |
| 272 | UNITED STATES OF AMERICA | 42.727839 | -72.929108 | 123430000 | 131527000 | 145162000 | 103377000.0 | 110101000.0 | 121564000.0 | 20052600 | ... | 9951640.0 | 35873400.0 | 37247000.0 | 41124300.0 | 32253500.0 | 33508100.0 | 37010000.0 | 3619890.0 | 3738910.0 | 4114260 |
| 273 | KOREA, REPUBLIC OF | 35.411130 | 126.416190 | 649425000 | 710033000 | 750775000 | 404604000.0 | 440983000.0 | 464987000.0 | 244821000 | ... | 35715500.0 | 42866100.0 | 46496100.0 | 48569700.0 | 36191700.0 | 39809100.0 | 41589500.0 | 6674350.0 | 6686990.0 | 6980200 |
| 274 | UKRAINE | 47.511809 | 34.585460 | 273432000 | 274834000 | 272561000 | 162035000.0 | 163493000.0 | 162347000.0 | 111397000 | ... | 22931000.0 | 20624400.0 | 19361900.0 | 17990600.0 | 13838800.0 | 12978800.0 | 12067100.0 | 6785590.0 | 6383160.0 | 5923510 |
| 275 | UNITED STATES OF AMERICA | 42.446428 | -87.803003 | 162525000 | 176001000 | 194245000 | 131460000.0 | 142393000.0 | 157233000.0 | 31065000 | ... | 11318900.0 | 19702800.0 | 21460200.0 | 23696900.0 | 16653600.0 | 18285600.0 | 20199900.0 | 3049180.0 | 3174560.0 | 3497080 |
276 rows × 57 columns
from sklearn.preprocessing import LabelEncoder
# Label Encoding
label_encoder = LabelEncoder() # Create a LabelEncoder instance
categorical_cols = [ 'Country'] # Specify the categorical columns to be encoded
for col in categorical_cols:
df[col + '_encoded'] = label_encoder.fit_transform(df[col]) # Apply label encoding to each column
print("Label Encoded Data:")
df
Label Encoded Data:
| Country | Latitude | Longitude | p90_1200 | p00_1200 | p10_1200 | p90u_1200 | p00u_1200 | p10u_1200 | p90r_1200 | ... | p90_300 | p00_300 | p10_300 | p90u_300 | p00u_300 | p10u_300 | p90r_300 | p00r_300 | p10r_300 | Country_encoded | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | SWEDEN | 59.206022 | 18.082872 | 187382000 | 188684000 | 188250000 | 121178000.0 | 122299000.0 | 122399000.0 | 66203700 | ... | 5013240.0 | 5227700.0 | 5471110.0 | 3426880.0 | 3596030.0 | 3764920.0 | 1586350.0 | 1631670.0 | 1706190 | 28 |
| 1 | SPAIN | 39.808100 | -5.696940 | 136675000 | 147718000 | 163429000 | 87539700.0 | 93943200.0 | 103704000.0 | 49135800 | ... | 17756500.0 | 18187800.0 | 20185200.0 | 10986000.0 | 11415400.0 | 12689800.0 | 6770480.0 | 6772380.0 | 7495340 | 27 |
| 2 | BRAZIL | -23.007857 | -44.458098 | 99195200 | 113894000 | 127898000 | 67751500.0 | 77821400.0 | 87432200.0 | 31443800 | ... | 39546400.0 | 44701700.0 | 50210600.0 | 32788500.0 | 37064600.0 | 41648300.0 | 6757940.0 | 7637110.0 | 8562300 | 3 |
| 3 | UNITED STATES OF AMERICA | 35.310320 | -93.231289 | 117830000 | 132729000 | 146482000 | 92609500.0 | 104781000.0 | 115697000.0 | 25220200 | ... | 5603180.0 | 6226360.0 | 6866840.0 | 3779400.0 | 4198920.0 | 4633770.0 | 1823770.0 | 2027450.0 | 2233070 | 33 |
| 4 | SPAIN | 41.200000 | 0.566670 | 271854000 | 287134000 | 308922000 | 190825000.0 | 200465000.0 | 215092000.0 | 81029500 | ... | 14398500.0 | 15095600.0 | 16830700.0 | 11215400.0 | 11773600.0 | 13151300.0 | 3183180.0 | 3322050.0 | 3679470 | 27 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 271 | CHINA | 21.708333 | 112.261111 | 557032000 | 629419000 | 679747000 | 250848000.0 | 287891000.0 | 309439000.0 | 306184000 | ... | 68115100.0 | 87136800.0 | 93428600.0 | 41415700.0 | 53433600.0 | 57372000.0 | 26699400.0 | 33703200.0 | 36056500 | 6 |
| 272 | UNITED STATES OF AMERICA | 42.727839 | -72.929108 | 123430000 | 131527000 | 145162000 | 103377000.0 | 110101000.0 | 121564000.0 | 20052600 | ... | 35873400.0 | 37247000.0 | 41124300.0 | 32253500.0 | 33508100.0 | 37010000.0 | 3619890.0 | 3738910.0 | 4114260 | 33 |
| 273 | KOREA, REPUBLIC OF | 35.411130 | 126.416190 | 649425000 | 710033000 | 750775000 | 404604000.0 | 440983000.0 | 464987000.0 | 244821000 | ... | 42866100.0 | 46496100.0 | 48569700.0 | 36191700.0 | 39809100.0 | 41589500.0 | 6674350.0 | 6686990.0 | 6980200 | 17 |
| 274 | UKRAINE | 47.511809 | 34.585460 | 273432000 | 274834000 | 272561000 | 162035000.0 | 163493000.0 | 162347000.0 | 111397000 | ... | 20624400.0 | 19361900.0 | 17990600.0 | 13838800.0 | 12978800.0 | 12067100.0 | 6785590.0 | 6383160.0 | 5923510 | 31 |
| 275 | UNITED STATES OF AMERICA | 42.446428 | -87.803003 | 162525000 | 176001000 | 194245000 | 131460000.0 | 142393000.0 | 157233000.0 | 31065000 | ... | 19702800.0 | 21460200.0 | 23696900.0 | 16653600.0 | 18285600.0 | 20199900.0 | 3049180.0 | 3174560.0 | 3497080 | 33 |
276 rows × 58 columns
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 276 entries, 0 to 275 Data columns (total 58 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Country 276 non-null object 1 Latitude 276 non-null float64 2 Longitude 276 non-null float64 3 p90_1200 276 non-null int64 4 p00_1200 276 non-null int64 5 p10_1200 276 non-null int64 6 p90u_1200 276 non-null float64 7 p00u_1200 276 non-null float64 8 p10u_1200 276 non-null float64 9 p90r_1200 276 non-null int64 10 p00r_1200 276 non-null int64 11 p10r_1200 276 non-null int64 12 p90_30 276 non-null float64 13 p00_30 276 non-null float64 14 p10_30 276 non-null float64 15 p90u_30 276 non-null float64 16 p00u_30 276 non-null float64 17 p10u_30 276 non-null float64 18 p90r_30 276 non-null float64 19 p00r_30 276 non-null float64 20 p10r_30 276 non-null float64 21 p90_75 276 non-null float64 22 p00_75 276 non-null float64 23 p10_75 276 non-null float64 24 p90u_75 276 non-null float64 25 p00u_75 276 non-null float64 26 p10u_75 276 non-null float64 27 p90r_75 276 non-null float64 28 p00r_75 276 non-null float64 29 p10r_75 276 non-null float64 30 p90_150 276 non-null float64 31 p00_150 276 non-null float64 32 p10_150 276 non-null float64 33 p90u_150 276 non-null float64 34 p00u_150 276 non-null float64 35 p10u_150 276 non-null float64 36 p90r_150 276 non-null float64 37 p00r_150 276 non-null float64 38 p10r_150 276 non-null float64 39 p90_600 276 non-null float64 40 p00_600 276 non-null float64 41 p10_600 276 non-null float64 42 p90u_600 276 non-null float64 43 p00u_600 276 non-null float64 44 p10u_600 276 non-null float64 45 p90r_600 276 non-null float64 46 p00r_600 276 non-null float64 47 p10r_600 276 non-null float64 48 p90_300 276 non-null float64 49 p00_300 276 non-null float64 50 p10_300 276 non-null float64 51 p90u_300 276 non-null float64 52 p00u_300 276 non-null float64 53 p10u_300 276 non-null float64 54 p90r_300 276 non-null float64 55 p00r_300 276 non-null float64 56 p10r_300 276 non-null int64 57 Country_encoded 276 non-null int32 dtypes: float64(49), int32(1), int64(7), object(1) memory usage: 124.1+ KB
df.describe()
| Latitude | Longitude | p90_1200 | p00_1200 | p10_1200 | p90u_1200 | p00u_1200 | p10u_1200 | p90r_1200 | p00r_1200 | ... | p90_300 | p00_300 | p10_300 | p90u_300 | p00u_300 | p10u_300 | p90r_300 | p00r_300 | p10r_300 | Country_encoded | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 276.000000 | 276.000000 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | ... | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 276.000000 |
| mean | 41.464873 | -0.833154 | 2.634335e+08 | 2.816791e+08 | 2.987191e+08 | 1.748656e+08 | 1.861499e+08 | 1.968991e+08 | 8.856783e+07 | 9.552691e+07 | ... | 3.052863e+07 | 3.270072e+07 | 3.456480e+07 | 2.238403e+07 | 2.387204e+07 | 2.517659e+07 | 8.144602e+06 | 8.828685e+06 | 9.388209e+06 | 21.347826 |
| std | 13.138551 | 75.441222 | 1.640597e+08 | 1.833881e+08 | 1.994596e+08 | 9.068376e+07 | 9.689716e+07 | 1.015562e+08 | 8.880513e+07 | 1.033398e+08 | ... | 2.180212e+07 | 2.422163e+07 | 2.590455e+07 | 1.593904e+07 | 1.695498e+07 | 1.764387e+07 | 8.890845e+06 | 1.066851e+07 | 1.208475e+07 | 11.002910 |
| min | -33.968002 | -124.209044 | 3.558760e+05 | 3.260370e+05 | 3.034580e+05 | 2.171250e+04 | 1.592310e+04 | 1.545940e+04 | 3.341640e+05 | 3.101140e+05 | ... | 3.586390e+04 | 3.641960e+04 | 3.453570e+04 | 7.970990e-01 | 7.348840e-01 | 6.874290e-01 | 3.586310e+04 | 3.641890e+04 | 3.453500e+04 | 0.000000 |
| 25% | 35.735645 | -77.396385 | 1.463155e+08 | 1.530615e+08 | 1.626702e+08 | 1.122075e+08 | 1.157828e+08 | 1.212025e+08 | 2.801438e+07 | 3.016952e+07 | ... | 1.344860e+07 | 1.439000e+07 | 1.516002e+07 | 8.849315e+06 | 1.001171e+07 | 1.033928e+07 | 2.499460e+06 | 2.590068e+06 | 2.853270e+06 | 10.000000 |
| 50% | 42.839200 | 4.720832 | 2.033275e+08 | 2.145620e+08 | 2.195720e+08 | 1.474715e+08 | 1.564245e+08 | 1.678680e+08 | 6.151855e+07 | 6.502695e+07 | ... | 2.455375e+07 | 2.584920e+07 | 2.758905e+07 | 1.755090e+07 | 1.924650e+07 | 2.069725e+07 | 5.178405e+06 | 5.339695e+06 | 5.597290e+06 | 23.000000 |
| 75% | 49.579176 | 30.497974 | 3.788732e+08 | 3.913535e+08 | 4.031740e+08 | 2.547372e+08 | 2.631975e+08 | 2.724995e+08 | 1.192925e+08 | 1.215025e+08 | ... | 4.479865e+07 | 4.662135e+07 | 4.966498e+07 | 3.533808e+07 | 3.770638e+07 | 3.972580e+07 | 1.017880e+07 | 1.043062e+07 | 1.063500e+07 | 33.000000 |
| max | 68.050658 | 166.539698 | 8.268430e+08 | 1.007240e+09 | 1.188970e+09 | 4.857500e+08 | 5.464730e+08 | 5.824590e+08 | 6.008490e+08 | 7.324380e+08 | ... | 1.154820e+08 | 1.531130e+08 | 1.784110e+08 | 6.816160e+07 | 7.082890e+07 | 7.296970e+07 | 7.945180e+07 | 1.031770e+08 | 1.202680e+08 | 33.000000 |
8 rows × 57 columns
z_scores = np.abs((df - np.mean(df,axis=0)) / np.std(df))
threshold = 3 # Adjust the threshold as needed
outliers_count = np.sum(z_scores > threshold)
print("Number of outliers:", outliers_count)
Number of outliers: Country 0 Country_encoded 0 Latitude 4 Longitude 0 p00_1200 4 p00_150 5 p00_30 8 p00_300 3 p00_600 4 p00_75 5 p00r_1200 4 p00r_150 3 p00r_30 4 p00r_300 5 p00r_600 6 p00r_75 4 p00u_1200 4 p00u_150 6 p00u_30 5 p00u_300 0 p00u_600 2 p00u_75 5 p10_1200 5 p10_150 5 p10_30 8 p10_300 3 p10_600 4 p10_75 6 p10r_1200 4 p10r_150 4 p10r_30 4 p10r_300 5 p10r_600 7 p10r_75 4 p10u_1200 4 p10u_150 7 p10u_30 5 p10u_300 0 p10u_600 2 p10u_75 5 p90_1200 4 p90_150 5 p90_30 6 p90_300 2 p90_600 3 p90_75 4 p90r_1200 4 p90r_150 3 p90r_30 5 p90r_300 5 p90r_600 5 p90r_75 3 p90u_1200 2 p90u_150 3 p90u_30 5 p90u_300 0 p90u_600 2 p90u_75 4 dtype: int64
C:\Users\prabha\Documents\Anaconda\lib\site-packages\numpy\core\fromnumeric.py:3430: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. return mean(axis=axis, dtype=dtype, out=out, **kwargs) C:\Users\prabha\Documents\Anaconda\lib\site-packages\numpy\core\fromnumeric.py:3571: FutureWarning: The default value of numeric_only in DataFrame.std is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)
num_cols = [
'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200',
'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30',
'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75',
'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150',
'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600',
'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300'
]
for col in num_cols:
# Calculate z-scores
z_scores = stats.zscore(df[col])
# Set the threshold for z-score outliers
threshold = 3
# Detect outliers
outliers = df[abs(z_scores) > threshold]
# Visualize outliers using box plots
plt.figure(figsize=(8, 6))
sns.boxplot(x=df[col])
plt.title(f"Boxplot of {col}")
plt.show()
# Print outlier details
print(f"Outliers in {col}:")
print(outliers)
Outliers in p90_1200:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
167 103177000.0 120268000 12
201 37271400.0 39811200 6
204 48117100.0 55996900 12
243 47354800.0 50583400 6
[4 rows x 58 columns]
Outliers in p00_1200:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
167 103177000.0 120268000 12
201 37271400.0 39811200 6
204 48117100.0 55996900 12
243 47354800.0 50583400 6
[4 rows x 58 columns]
Outliers in p10_1200:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
123 48695900.0 56664800 12
167 103177000.0 120268000 12
201 37271400.0 39811200 6
204 48117100.0 55996900 12
243 47354800.0 50583400 6
[5 rows x 58 columns]
Outliers in p90u_1200:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
201 37271400.0 39811200 6
243 47354800.0 50583400 6
[2 rows x 58 columns]
Outliers in p00u_1200:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
101 CHINA 36.708333 121.383333 742392000 820894000 873598000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
214 CHINA 29.101111 121.641944 722862000 811896000 865502000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
101 431762000.0 476751000.0 507009000.0 310629000 ... 36147200.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
214 441405000.0 496909000.0 529558000.0 281457000 ... 60464400.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
101 39052100.0 41715800.0 20213900.0 21771800.0 23258100.0 15933300.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
214 68482800.0 73153500.0 40739400.0 46073000.0 49217400.0 19725000.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
101 17280300.0 18457700 6
201 37271400.0 39811200 6
214 22409800.0 23936100 6
243 47354800.0 50583400 6
[4 rows x 58 columns]
Outliers in p10u_1200:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
101 CHINA 36.708333 121.383333 742392000 820894000 873598000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
214 CHINA 29.101111 121.641944 722862000 811896000 865502000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
101 431762000.0 476751000.0 507009000.0 310629000 ... 36147200.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
214 441405000.0 496909000.0 529558000.0 281457000 ... 60464400.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
101 39052100.0 41715800.0 20213900.0 21771800.0 23258100.0 15933300.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
214 68482800.0 73153500.0 40739400.0 46073000.0 49217400.0 19725000.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
101 17280300.0 18457700 6
201 37271400.0 39811200 6
214 22409800.0 23936100 6
243 47354800.0 50583400 6
[4 rows x 58 columns]
Outliers in p90r_1200:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
239 INDIA 19.828785 72.661201 568977000 700672000 820686000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
239 184165000.0 230100000.0 269807000.0 384812000 ... 53975200.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
239 69925300.0 81418700.0 28747300.0 37171700.0 43307300.0 25227900.0
p00r_300 p10r_300 Country_encoded
123 48695900.0 56664800 12
167 103177000.0 120268000 12
204 48117100.0 55996900 12
239 32753500.0 38111400 12
[4 rows x 58 columns]
Outliers in p00r_1200:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
239 INDIA 19.828785 72.661201 568977000 700672000 820686000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
239 184165000.0 230100000.0 269807000.0 384812000 ... 53975200.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
239 69925300.0 81418700.0 28747300.0 37171700.0 43307300.0 25227900.0
p00r_300 p10r_300 Country_encoded
123 48695900.0 56664800 12
167 103177000.0 120268000 12
204 48117100.0 55996900 12
239 32753500.0 38111400 12
[4 rows x 58 columns]
Outliers in p10r_1200:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
239 INDIA 19.828785 72.661201 568977000 700672000 820686000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
239 184165000.0 230100000.0 269807000.0 384812000 ... 53975200.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
239 69925300.0 81418700.0 28747300.0 37171700.0 43307300.0 25227900.0
p00r_300 p10r_300 Country_encoded
123 48695900.0 56664800 12
167 103177000.0 120268000 12
204 48117100.0 55996900 12
239 32753500.0 38111400 12
[4 rows x 58 columns]
Outliers in p90_30:
Country Latitude Longitude p90_1200 p00_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000
45 TAIWAN, CHINA 25.286254 121.587677 517341000 593088000
125 PAKISTAN 24.845343 66.788517 362335000 468951000
132 KOREA, REPUBLIC OF 35.321269 129.294517 503755000 544728000
137 TAIWAN, CHINA 25.202723 121.662638 511038000 586271000
260 RUSSIAN FEDERATION 59.931389 30.258056 141911000 137868000
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... \
39 829594000 393377000.0 436918000.0 465857000.0 307954000 ...
45 637718000 302987000.0 348317000.0 374429000.0 214354000 ...
125 562120000 130255000.0 170868000.0 204231000.0 232080000 ...
132 572227000 335409000.0 362093000.0 379209000.0 168346000 ...
137 630625000 300245000.0 345428000.0 371500000.0 210794000 ...
260 133762000 92412000.0 89938100.0 87392900.0 49498600 ...
p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 \
39 81160100.0 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0
45 29644200.0 34080400.0 36758700.0 23587100.0 27024000.0 29176500.0
125 19337200.0 25170100.0 31379300.0 11223700.0 14772100.0 18487500.0
132 36899100.0 36583300.0 37780800.0 28302400.0 28161400.0 29099900.0
137 28023200.0 32218000.0 34776300.0 22612000.0 25923200.0 28007300.0
260 9019310.0 8281210.0 8047670.0 7446810.0 6826140.0 6633940.0
p90r_300 p00r_300 p10r_300 Country_encoded
39 33588400.0 37924300.0 40505100 6
45 6057090.0 7056470.0 7582200 30
125 8113520.0 10397900.0 12891800 21
132 8596700.0 8421880.0 8680890 17
137 5411170.0 6294880.0 6769010 30
260 1572500.0 1455070.0 1413740 23
[6 rows x 58 columns]
Outliers in p00_30:
Country Latitude Longitude p90_1200 p00_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000
45 TAIWAN, CHINA 25.286254 121.587677 517341000 593088000
97 CHINA 22.597144 114.543139 615931000 702252000
125 PAKISTAN 24.845343 66.788517 362335000 468951000
132 KOREA, REPUBLIC OF 35.321269 129.294517 503755000 544728000
137 TAIWAN, CHINA 25.202723 121.662638 511038000 586271000
147 CHINA 22.606110 114.553247 616314000 702669000
260 RUSSIAN FEDERATION 59.931389 30.258056 141911000 137868000
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... \
39 829594000 393377000.0 436918000.0 465857000.0 307954000 ...
45 637718000 302987000.0 348317000.0 374429000.0 214354000 ...
97 758819000 314938000.0 362457000.0 390888000.0 300993000 ...
125 562120000 130255000.0 170868000.0 204231000.0 232080000 ...
132 572227000 335409000.0 362093000.0 379209000.0 168346000 ...
137 630625000 300245000.0 345428000.0 371500000.0 210794000 ...
147 759257000 315206000.0 362753000.0 391203000.0 301107000 ...
260 133762000 92412000.0 89938100.0 87392900.0 49498600 ...
p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 \
39 81160100.0 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0
45 29644200.0 34080400.0 36758700.0 23587100.0 27024000.0 29176500.0
97 64757000.0 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0
125 19337200.0 25170100.0 31379300.0 11223700.0 14772100.0 18487500.0
132 36899100.0 36583300.0 37780800.0 28302400.0 28161400.0 29099900.0
137 28023200.0 32218000.0 34776300.0 22612000.0 25923200.0 28007300.0
147 64798900.0 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0
260 9019310.0 8281210.0 8047670.0 7446810.0 6826140.0 6633940.0
p90r_300 p00r_300 p10r_300 Country_encoded
39 33588400.0 37924300.0 40505100 6
45 6057090.0 7056470.0 7582200 30
97 19904500.0 26459600.0 28317000 6
125 8113520.0 10397900.0 12891800 21
132 8596700.0 8421880.0 8680890 17
137 5411170.0 6294880.0 6769010 30
147 19925000.0 26481800.0 28340700 6
260 1572500.0 1455070.0 1413740 23
[8 rows x 58 columns]
Outliers in p10_30:
Country Latitude Longitude p90_1200 p00_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000
45 TAIWAN, CHINA 25.286254 121.587677 517341000 593088000
97 CHINA 22.597144 114.543139 615931000 702252000
125 PAKISTAN 24.845343 66.788517 362335000 468951000
132 KOREA, REPUBLIC OF 35.321269 129.294517 503755000 544728000
137 TAIWAN, CHINA 25.202723 121.662638 511038000 586271000
147 CHINA 22.606110 114.553247 616314000 702669000
260 RUSSIAN FEDERATION 59.931389 30.258056 141911000 137868000
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... \
39 829594000 393377000.0 436918000.0 465857000.0 307954000 ...
45 637718000 302987000.0 348317000.0 374429000.0 214354000 ...
97 758819000 314938000.0 362457000.0 390888000.0 300993000 ...
125 562120000 130255000.0 170868000.0 204231000.0 232080000 ...
132 572227000 335409000.0 362093000.0 379209000.0 168346000 ...
137 630625000 300245000.0 345428000.0 371500000.0 210794000 ...
147 759257000 315206000.0 362753000.0 391203000.0 301107000 ...
260 133762000 92412000.0 89938100.0 87392900.0 49498600 ...
p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 \
39 81160100.0 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0
45 29644200.0 34080400.0 36758700.0 23587100.0 27024000.0 29176500.0
97 64757000.0 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0
125 19337200.0 25170100.0 31379300.0 11223700.0 14772100.0 18487500.0
132 36899100.0 36583300.0 37780800.0 28302400.0 28161400.0 29099900.0
137 28023200.0 32218000.0 34776300.0 22612000.0 25923200.0 28007300.0
147 64798900.0 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0
260 9019310.0 8281210.0 8047670.0 7446810.0 6826140.0 6633940.0
p90r_300 p00r_300 p10r_300 Country_encoded
39 33588400.0 37924300.0 40505100 6
45 6057090.0 7056470.0 7582200 30
97 19904500.0 26459600.0 28317000 6
125 8113520.0 10397900.0 12891800 21
132 8596700.0 8421880.0 8680890 17
137 5411170.0 6294880.0 6769010 30
147 19925000.0 26481800.0 28340700 6
260 1572500.0 1455070.0 1413740 23
[8 rows x 58 columns]
Outliers in p90u_30:
Country Latitude Longitude p90_1200 p00_1200 \
45 TAIWAN, CHINA 25.286254 121.587677 517341000 593088000
125 PAKISTAN 24.845343 66.788517 362335000 468951000
132 KOREA, REPUBLIC OF 35.321269 129.294517 503755000 544728000
137 TAIWAN, CHINA 25.202723 121.662638 511038000 586271000
260 RUSSIAN FEDERATION 59.931389 30.258056 141911000 137868000
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... \
45 637718000 302987000.0 348317000.0 374429000.0 214354000 ...
125 562120000 130255000.0 170868000.0 204231000.0 232080000 ...
132 572227000 335409000.0 362093000.0 379209000.0 168346000 ...
137 630625000 300245000.0 345428000.0 371500000.0 210794000 ...
260 133762000 92412000.0 89938100.0 87392900.0 49498600 ...
p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 \
45 29644200.0 34080400.0 36758700.0 23587100.0 27024000.0 29176500.0
125 19337200.0 25170100.0 31379300.0 11223700.0 14772100.0 18487500.0
132 36899100.0 36583300.0 37780800.0 28302400.0 28161400.0 29099900.0
137 28023200.0 32218000.0 34776300.0 22612000.0 25923200.0 28007300.0
260 9019310.0 8281210.0 8047670.0 7446810.0 6826140.0 6633940.0
p90r_300 p00r_300 p10r_300 Country_encoded
45 6057090.0 7056470.0 7582200 30
125 8113520.0 10397900.0 12891800 21
132 8596700.0 8421880.0 8680890 17
137 5411170.0 6294880.0 6769010 30
260 1572500.0 1455070.0 1413740 23
[5 rows x 58 columns]
Outliers in p00u_30:
Country Latitude Longitude p90_1200 p00_1200 \
45 TAIWAN, CHINA 25.286254 121.587677 517341000 593088000
125 PAKISTAN 24.845343 66.788517 362335000 468951000
132 KOREA, REPUBLIC OF 35.321269 129.294517 503755000 544728000
137 TAIWAN, CHINA 25.202723 121.662638 511038000 586271000
260 RUSSIAN FEDERATION 59.931389 30.258056 141911000 137868000
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... \
45 637718000 302987000.0 348317000.0 374429000.0 214354000 ...
125 562120000 130255000.0 170868000.0 204231000.0 232080000 ...
132 572227000 335409000.0 362093000.0 379209000.0 168346000 ...
137 630625000 300245000.0 345428000.0 371500000.0 210794000 ...
260 133762000 92412000.0 89938100.0 87392900.0 49498600 ...
p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 \
45 29644200.0 34080400.0 36758700.0 23587100.0 27024000.0 29176500.0
125 19337200.0 25170100.0 31379300.0 11223700.0 14772100.0 18487500.0
132 36899100.0 36583300.0 37780800.0 28302400.0 28161400.0 29099900.0
137 28023200.0 32218000.0 34776300.0 22612000.0 25923200.0 28007300.0
260 9019310.0 8281210.0 8047670.0 7446810.0 6826140.0 6633940.0
p90r_300 p00r_300 p10r_300 Country_encoded
45 6057090.0 7056470.0 7582200 30
125 8113520.0 10397900.0 12891800 21
132 8596700.0 8421880.0 8680890 17
137 5411170.0 6294880.0 6769010 30
260 1572500.0 1455070.0 1413740 23
[5 rows x 58 columns]
Outliers in p10u_30:
Country Latitude Longitude p90_1200 p00_1200 \
45 TAIWAN, CHINA 25.286254 121.587677 517341000 593088000
125 PAKISTAN 24.845343 66.788517 362335000 468951000
132 KOREA, REPUBLIC OF 35.321269 129.294517 503755000 544728000
137 TAIWAN, CHINA 25.202723 121.662638 511038000 586271000
260 RUSSIAN FEDERATION 59.931389 30.258056 141911000 137868000
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... \
45 637718000 302987000.0 348317000.0 374429000.0 214354000 ...
125 562120000 130255000.0 170868000.0 204231000.0 232080000 ...
132 572227000 335409000.0 362093000.0 379209000.0 168346000 ...
137 630625000 300245000.0 345428000.0 371500000.0 210794000 ...
260 133762000 92412000.0 89938100.0 87392900.0 49498600 ...
p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 \
45 29644200.0 34080400.0 36758700.0 23587100.0 27024000.0 29176500.0
125 19337200.0 25170100.0 31379300.0 11223700.0 14772100.0 18487500.0
132 36899100.0 36583300.0 37780800.0 28302400.0 28161400.0 29099900.0
137 28023200.0 32218000.0 34776300.0 22612000.0 25923200.0 28007300.0
260 9019310.0 8281210.0 8047670.0 7446810.0 6826140.0 6633940.0
p90r_300 p00r_300 p10r_300 Country_encoded
45 6057090.0 7056470.0 7582200 30
125 8113520.0 10397900.0 12891800 21
132 8596700.0 8421880.0 8680890 17
137 5411170.0 6294880.0 6769010 30
260 1572500.0 1455070.0 1413740 23
[5 rows x 58 columns]
Outliers in p90r_30:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000 829594000
97 CHINA 22.597144 114.543139 615931000 702252000 758819000
147 CHINA 22.606110 114.553247 616314000 702669000 759257000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
39 393377000.0 436918000.0 465857000.0 307954000 ... 81160100.0
97 314938000.0 362457000.0 390888000.0 300993000 ... 64757000.0
147 315206000.0 362753000.0 391203000.0 301107000 ... 64798900.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
39 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0 33588400.0
97 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0 19904500.0
147 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0 19925000.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
p00r_300 p10r_300 Country_encoded
39 37924300.0 40505100 6
97 26459600.0 28317000 6
147 26481800.0 28340700 6
167 103177000.0 120268000 12
201 37271400.0 39811200 6
[5 rows x 58 columns]
Outliers in p00r_30:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000 829594000
97 CHINA 22.597144 114.543139 615931000 702252000 758819000
147 CHINA 22.606110 114.553247 616314000 702669000 759257000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
39 393377000.0 436918000.0 465857000.0 307954000 ... 81160100.0
97 314938000.0 362457000.0 390888000.0 300993000 ... 64757000.0
147 315206000.0 362753000.0 391203000.0 301107000 ... 64798900.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
39 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0 33588400.0
97 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0 19904500.0
147 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0 19925000.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
p00r_300 p10r_300 Country_encoded
39 37924300.0 40505100 6
97 26459600.0 28317000 6
147 26481800.0 28340700 6
167 103177000.0 120268000 12
[4 rows x 58 columns]
Outliers in p10r_30:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000 829594000
97 CHINA 22.597144 114.543139 615931000 702252000 758819000
147 CHINA 22.606110 114.553247 616314000 702669000 759257000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
39 393377000.0 436918000.0 465857000.0 307954000 ... 81160100.0
97 314938000.0 362457000.0 390888000.0 300993000 ... 64757000.0
147 315206000.0 362753000.0 391203000.0 301107000 ... 64798900.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
39 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0 33588400.0
97 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0 19904500.0
147 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0 19925000.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
p00r_300 p10r_300 Country_encoded
39 37924300.0 40505100 6
97 26459600.0 28317000 6
147 26481800.0 28340700 6
167 103177000.0 120268000 12
[4 rows x 58 columns]
Outliers in p90_75:
Country Latitude Longitude p90_1200 p00_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000
97 CHINA 22.597144 114.543139 615931000 702252000
118 UNITED STATES OF AMERICA 41.271400 -73.952500 138491000 148816000
147 CHINA 22.606110 114.553247 616314000 702669000
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... \
39 829594000 393377000.0 436918000.0 465857000.0 307954000 ...
97 758819000 314938000.0 362457000.0 390888000.0 300993000 ...
118 164264000 116270000.0 124903000.0 137925000.0 22220800 ...
147 759257000 315206000.0 362753000.0 391203000.0 301107000 ...
p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 \
39 81160100.0 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0
97 64757000.0 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0
118 43882200.0 45617300.0 50369700.0 39721900.0 41334900.0 45655000.0
147 64798900.0 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0
p90r_300 p00r_300 p10r_300 Country_encoded
39 33588400.0 37924300.0 40505100 6
97 19904500.0 26459600.0 28317000 6
118 4160310.0 4282350.0 4714700 33
147 19925000.0 26481800.0 28340700 6
[4 rows x 58 columns]
Outliers in p00_75:
Country Latitude Longitude p90_1200 p00_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000
97 CHINA 22.597144 114.543139 615931000 702252000
118 UNITED STATES OF AMERICA 41.271400 -73.952500 138491000 148816000
147 CHINA 22.606110 114.553247 616314000 702669000
167 INDIA 28.159867 78.408658 818999000 1007240000
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... \
39 829594000 393377000.0 436918000.0 465857000.0 307954000 ...
97 758819000 314938000.0 362457000.0 390888000.0 300993000 ...
118 164264000 116270000.0 124903000.0 137925000.0 22220800 ...
147 759257000 315206000.0 362753000.0 391203000.0 301107000 ...
167 1188970000 218150000.0 274801000.0 324681000.0 600849000 ...
p90_300 p00_300 p10_300 p90u_300 p00u_300 \
39 81160100.0 91306200.0 97528200.0 47571700.0 53381900.0
97 64757000.0 85134500.0 91288100.0 44852500.0 58674900.0
118 43882200.0 45617300.0 50369700.0 39721900.0 41334900.0
147 64798900.0 85178800.0 91335400.0 44873900.0 58697000.0
167 115482000.0 153113000.0 178411000.0 36030100.0 49936000.0
p10u_300 p90r_300 p00r_300 p10r_300 Country_encoded
39 57023100.0 33588400.0 37924300.0 40505100 6
97 62971100.0 19904500.0 26459600.0 28317000 6
118 45655000.0 4160310.0 4282350.0 4714700 33
147 62994700.0 19925000.0 26481800.0 28340700 6
167 58143700.0 79451800.0 103177000.0 120268000 12
[5 rows x 58 columns]
Outliers in p10_75:
Country Latitude Longitude p90_1200 p00_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000
97 CHINA 22.597144 114.543139 615931000 702252000
118 UNITED STATES OF AMERICA 41.271400 -73.952500 138491000 148816000
125 PAKISTAN 24.845343 66.788517 362335000 468951000
147 CHINA 22.606110 114.553247 616314000 702669000
167 INDIA 28.159867 78.408658 818999000 1007240000
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... \
39 829594000 393377000.0 436918000.0 465857000.0 307954000 ...
97 758819000 314938000.0 362457000.0 390888000.0 300993000 ...
118 164264000 116270000.0 124903000.0 137925000.0 22220800 ...
125 562120000 130255000.0 170868000.0 204231000.0 232080000 ...
147 759257000 315206000.0 362753000.0 391203000.0 301107000 ...
167 1188970000 218150000.0 274801000.0 324681000.0 600849000 ...
p90_300 p00_300 p10_300 p90u_300 p00u_300 \
39 81160100.0 91306200.0 97528200.0 47571700.0 53381900.0
97 64757000.0 85134500.0 91288100.0 44852500.0 58674900.0
118 43882200.0 45617300.0 50369700.0 39721900.0 41334900.0
125 19337200.0 25170100.0 31379300.0 11223700.0 14772100.0
147 64798900.0 85178800.0 91335400.0 44873900.0 58697000.0
167 115482000.0 153113000.0 178411000.0 36030100.0 49936000.0
p10u_300 p90r_300 p00r_300 p10r_300 Country_encoded
39 57023100.0 33588400.0 37924300.0 40505100 6
97 62971100.0 19904500.0 26459600.0 28317000 6
118 45655000.0 4160310.0 4282350.0 4714700 33
125 18487500.0 8113520.0 10397900.0 12891800 21
147 62994700.0 19925000.0 26481800.0 28340700 6
167 58143700.0 79451800.0 103177000.0 120268000 12
[6 rows x 58 columns]
Outliers in p90u_75:
Country Latitude Longitude p90_1200 p00_1200 \
6 GERMANY 50.903056 6.421111 377877000 387602000
97 CHINA 22.597144 114.543139 615931000 702252000
118 UNITED STATES OF AMERICA 41.271400 -73.952500 138491000 148816000
147 CHINA 22.606110 114.553247 616314000 702669000
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... \
6 400550000 265023000.0 272376000.0 282369000.0 112854000 ...
97 758819000 314938000.0 362457000.0 390888000.0 300993000 ...
118 164264000 116270000.0 124903000.0 137925000.0 22220800 ...
147 759257000 315206000.0 362753000.0 391203000.0 301107000 ...
p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 \
6 71747700.0 75061700.0 76803300.0 55377400.0 57904900.0 59294500.0
97 64757000.0 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0
118 43882200.0 45617300.0 50369700.0 39721900.0 41334900.0 45655000.0
147 64798900.0 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0
p90r_300 p00r_300 p10r_300 Country_encoded
6 16370200.0 17156800.0 17508800 10
97 19904500.0 26459600.0 28317000 6
118 4160310.0 4282350.0 4714700 33
147 19925000.0 26481800.0 28340700 6
[4 rows x 58 columns]
Outliers in p00u_75:
Country Latitude Longitude p90_1200 p00_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000
97 CHINA 22.597144 114.543139 615931000 702252000
118 UNITED STATES OF AMERICA 41.271400 -73.952500 138491000 148816000
125 PAKISTAN 24.845343 66.788517 362335000 468951000
147 CHINA 22.606110 114.553247 616314000 702669000
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... \
39 829594000 393377000.0 436918000.0 465857000.0 307954000 ...
97 758819000 314938000.0 362457000.0 390888000.0 300993000 ...
118 164264000 116270000.0 124903000.0 137925000.0 22220800 ...
125 562120000 130255000.0 170868000.0 204231000.0 232080000 ...
147 759257000 315206000.0 362753000.0 391203000.0 301107000 ...
p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 \
39 81160100.0 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0
97 64757000.0 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0
118 43882200.0 45617300.0 50369700.0 39721900.0 41334900.0 45655000.0
125 19337200.0 25170100.0 31379300.0 11223700.0 14772100.0 18487500.0
147 64798900.0 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0
p90r_300 p00r_300 p10r_300 Country_encoded
39 33588400.0 37924300.0 40505100 6
97 19904500.0 26459600.0 28317000 6
118 4160310.0 4282350.0 4714700 33
125 8113520.0 10397900.0 12891800 21
147 19925000.0 26481800.0 28340700 6
[5 rows x 58 columns]
Outliers in p10u_75:
Country Latitude Longitude p90_1200 p00_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000
97 CHINA 22.597144 114.543139 615931000 702252000
118 UNITED STATES OF AMERICA 41.271400 -73.952500 138491000 148816000
125 PAKISTAN 24.845343 66.788517 362335000 468951000
147 CHINA 22.606110 114.553247 616314000 702669000
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... \
39 829594000 393377000.0 436918000.0 465857000.0 307954000 ...
97 758819000 314938000.0 362457000.0 390888000.0 300993000 ...
118 164264000 116270000.0 124903000.0 137925000.0 22220800 ...
125 562120000 130255000.0 170868000.0 204231000.0 232080000 ...
147 759257000 315206000.0 362753000.0 391203000.0 301107000 ...
p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 \
39 81160100.0 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0
97 64757000.0 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0
118 43882200.0 45617300.0 50369700.0 39721900.0 41334900.0 45655000.0
125 19337200.0 25170100.0 31379300.0 11223700.0 14772100.0 18487500.0
147 64798900.0 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0
p90r_300 p00r_300 p10r_300 Country_encoded
39 33588400.0 37924300.0 40505100 6
97 19904500.0 26459600.0 28317000 6
118 4160310.0 4282350.0 4714700 33
125 8113520.0 10397900.0 12891800 21
147 19925000.0 26481800.0 28340700 6
[5 rows x 58 columns]
Outliers in p90r_75:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000 829594000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
39 393377000.0 436918000.0 465857000.0 307954000 ... 81160100.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
39 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0 33588400.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
p00r_300 p10r_300 Country_encoded
39 37924300.0 40505100 6
167 103177000.0 120268000 12
201 37271400.0 39811200 6
[3 rows x 58 columns]
Outliers in p00r_75:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000 829594000
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
39 393377000.0 436918000.0 465857000.0 307954000 ... 81160100.0
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
39 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0 33588400.0
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
p00r_300 p10r_300 Country_encoded
39 37924300.0 40505100 6
123 48695900.0 56664800 12
167 103177000.0 120268000 12
201 37271400.0 39811200 6
[4 rows x 58 columns]
Outliers in p10r_75:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000 829594000
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
39 393377000.0 436918000.0 465857000.0 307954000 ... 81160100.0
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
39 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0 33588400.0
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
p00r_300 p10r_300 Country_encoded
39 37924300.0 40505100 6
123 48695900.0 56664800 12
167 103177000.0 120268000 12
201 37271400.0 39811200 6
[4 rows x 58 columns]
Outliers in p90_150:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
97 CHINA 22.597144 114.543139 615931000 702252000 758819000
121 JAPAN 36.458461 140.606342 150088000 153878000 155325000
147 CHINA 22.606110 114.553247 616314000 702669000 759257000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
97 314938000.0 362457000.0 390888000.0 300993000 ... 64757000.0
121 116253000.0 119213000.0 120223000.0 33834900 ... 52772100.0
147 315206000.0 362753000.0 391203000.0 301107000 ... 64798900.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
97 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0 19904500.0
121 55009400.0 55137900.0 43514200.0 45459400.0 45567000.0 9257910.0
147 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0 19925000.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
p00r_300 p10r_300 Country_encoded
97 26459600.0 28317000 6
121 9550020.0 9570870 15
147 26481800.0 28340700 6
167 103177000.0 120268000 12
201 37271400.0 39811200 6
[5 rows x 58 columns]
Outliers in p00_150:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
97 CHINA 22.597144 114.543139 615931000 702252000 758819000
147 CHINA 22.606110 114.553247 616314000 702669000 759257000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
237 CHINA 21.917778 112.981944 575483000 653428000 707083000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
97 314938000.0 362457000.0 390888000.0 300993000 ... 64757000.0
147 315206000.0 362753000.0 391203000.0 301107000 ... 64798900.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
237 272990000.0 314491000.0 339614000.0 302493000 ... 61722200.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
97 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0 19904500.0
147 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0 19925000.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
237 80251900.0 86074100.0 39459400.0 51303500.0 55097200.0 22262800.0
p00r_300 p10r_300 Country_encoded
97 26459600.0 28317000 6
147 26481800.0 28340700 6
167 103177000.0 120268000 12
201 37271400.0 39811200 6
237 28948400.0 30977000 6
[5 rows x 58 columns]
Outliers in p10_150:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
97 CHINA 22.597144 114.543139 615931000 702252000 758819000
147 CHINA 22.606110 114.553247 616314000 702669000 759257000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
237 CHINA 21.917778 112.981944 575483000 653428000 707083000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
97 314938000.0 362457000.0 390888000.0 300993000 ... 64757000.0
147 315206000.0 362753000.0 391203000.0 301107000 ... 64798900.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
237 272990000.0 314491000.0 339614000.0 302493000 ... 61722200.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
97 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0 19904500.0
147 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0 19925000.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
237 80251900.0 86074100.0 39459400.0 51303500.0 55097200.0 22262800.0
p00r_300 p10r_300 Country_encoded
97 26459600.0 28317000 6
147 26481800.0 28340700 6
167 103177000.0 120268000 12
201 37271400.0 39811200 6
237 28948400.0 30977000 6
[5 rows x 58 columns]
Outliers in p90u_150:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
121 JAPAN 36.458461 140.606342 150088000 153878000 155325000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
245 JAPAN 36.466624 140.609604 150073000 153861000 155307000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
121 116253000.0 119213000.0 120223000.0 33834900 ... 52772100.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
245 116229000.0 119186000.0 120195000.0 33843900 ... 52762100.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
121 55009400.0 55137900.0 43514200.0 45459400.0 45567000.0 9257910.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
245 54998500.0 55127000.0 43501900.0 45446600.0 45554200.0 9260260.0
p00r_300 p10r_300 Country_encoded
121 9550020.0 9570870 15
201 37271400.0 39811200 6
245 9551980.0 9572840 15
[3 rows x 58 columns]
Outliers in p00u_150:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
97 CHINA 22.597144 114.543139 615931000 702252000 758819000
121 JAPAN 36.458461 140.606342 150088000 153878000 155325000
147 CHINA 22.606110 114.553247 616314000 702669000 759257000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
237 CHINA 21.917778 112.981944 575483000 653428000 707083000
245 JAPAN 36.466624 140.609604 150073000 153861000 155307000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
97 314938000.0 362457000.0 390888000.0 300993000 ... 64757000.0
121 116253000.0 119213000.0 120223000.0 33834900 ... 52772100.0
147 315206000.0 362753000.0 391203000.0 301107000 ... 64798900.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
237 272990000.0 314491000.0 339614000.0 302493000 ... 61722200.0
245 116229000.0 119186000.0 120195000.0 33843900 ... 52762100.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
97 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0 19904500.0
121 55009400.0 55137900.0 43514200.0 45459400.0 45567000.0 9257910.0
147 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0 19925000.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
237 80251900.0 86074100.0 39459400.0 51303500.0 55097200.0 22262800.0
245 54998500.0 55127000.0 43501900.0 45446600.0 45554200.0 9260260.0
p00r_300 p10r_300 Country_encoded
97 26459600.0 28317000 6
121 9550020.0 9570870 15
147 26481800.0 28340700 6
201 37271400.0 39811200 6
237 28948400.0 30977000 6
245 9551980.0 9572840 15
[6 rows x 58 columns]
Outliers in p10u_150:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
97 CHINA 22.597144 114.543139 615931000 702252000 758819000
121 JAPAN 36.458461 140.606342 150088000 153878000 155325000
147 CHINA 22.606110 114.553247 616314000 702669000 759257000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
237 CHINA 21.917778 112.981944 575483000 653428000 707083000
245 JAPAN 36.466624 140.609604 150073000 153861000 155307000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
97 314938000.0 362457000.0 390888000.0 300993000 ... 64757000.0
121 116253000.0 119213000.0 120223000.0 33834900 ... 52772100.0
147 315206000.0 362753000.0 391203000.0 301107000 ... 64798900.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
237 272990000.0 314491000.0 339614000.0 302493000 ... 61722200.0
245 116229000.0 119186000.0 120195000.0 33843900 ... 52762100.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
97 85134500.0 91288100.0 44852500.0 58674900.0 62971100.0 19904500.0
121 55009400.0 55137900.0 43514200.0 45459400.0 45567000.0 9257910.0
147 85178800.0 91335400.0 44873900.0 58697000.0 62994700.0 19925000.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
237 80251900.0 86074100.0 39459400.0 51303500.0 55097200.0 22262800.0
245 54998500.0 55127000.0 43501900.0 45446600.0 45554200.0 9260260.0
p00r_300 p10r_300 Country_encoded
97 26459600.0 28317000 6
121 9550020.0 9570870 15
147 26481800.0 28340700 6
167 103177000.0 120268000 12
201 37271400.0 39811200 6
237 28948400.0 30977000 6
245 9551980.0 9572840 15
[7 rows x 58 columns]
Outliers in p90r_150:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000 829594000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
39 393377000.0 436918000.0 465857000.0 307954000 ... 81160100.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
39 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0 33588400.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
p00r_300 p10r_300 Country_encoded
39 37924300.0 40505100 6
167 103177000.0 120268000 12
201 37271400.0 39811200 6
[3 rows x 58 columns]
Outliers in p00r_150:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000 829594000
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
39 393377000.0 436918000.0 465857000.0 307954000 ... 81160100.0
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
39 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0 33588400.0
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
p00r_300 p10r_300 Country_encoded
39 37924300.0 40505100 6
123 48695900.0 56664800 12
167 103177000.0 120268000 12
[3 rows x 58 columns]
Outliers in p10r_150:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
39 CHINA 39.740929 116.030139 701332000 777851000 829594000
43 PAKISTAN 32.390929 71.463095 424183000 546171000 651723000
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
39 393377000.0 436918000.0 465857000.0 307954000 ... 81160100.0
43 131066000.0 169058000.0 200907000.0 293117000 ... 70198600.0
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
39 91306200.0 97528200.0 47571700.0 53381900.0 57023100.0 33588400.0
43 89264100.0 111771000.0 21823700.0 27781300.0 34699400.0 48374900.0
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
p00r_300 p10r_300 Country_encoded
39 37924300.0 40505100 6
43 61482800.0 77071200 21
123 48695900.0 56664800 12
167 103177000.0 120268000 12
[4 rows x 58 columns]
Outliers in p90_600:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
101 CHINA 36.708333 121.383333 742392000 820894000 873598000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
101 431762000.0 476751000.0 507009000.0 310629000 ... 36147200.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
101 39052100.0 41715800.0 20213900.0 21771800.0 23258100.0 15933300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
101 17280300.0 18457700 6
167 103177000.0 120268000 12
243 47354800.0 50583400 6
[3 rows x 58 columns]
Outliers in p00_600:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
101 CHINA 36.708333 121.383333 742392000 820894000 873598000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
101 431762000.0 476751000.0 507009000.0 310629000 ... 36147200.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
101 39052100.0 41715800.0 20213900.0 21771800.0 23258100.0 15933300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
101 17280300.0 18457700 6
167 103177000.0 120268000 12
204 48117100.0 55996900 12
243 47354800.0 50583400 6
[4 rows x 58 columns]
Outliers in p10_600:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
101 CHINA 36.708333 121.383333 742392000 820894000 873598000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
101 431762000.0 476751000.0 507009000.0 310629000 ... 36147200.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
101 39052100.0 41715800.0 20213900.0 21771800.0 23258100.0 15933300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
101 17280300.0 18457700 6
167 103177000.0 120268000 12
204 48117100.0 55996900 12
243 47354800.0 50583400 6
[4 rows x 58 columns]
Outliers in p90u_600:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
101 CHINA 36.708333 121.383333 742392000 820894000 873598000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
101 431762000.0 476751000.0 507009000.0 310629000 ... 36147200.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
101 39052100.0 41715800.0 20213900.0 21771800.0 23258100.0 15933300.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
101 17280300.0 18457700 6
243 47354800.0 50583400 6
[2 rows x 58 columns]
Outliers in p00u_600:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
101 CHINA 36.708333 121.383333 742392000 820894000 873598000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
101 431762000.0 476751000.0 507009000.0 310629000 ... 36147200.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
101 39052100.0 41715800.0 20213900.0 21771800.0 23258100.0 15933300.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
101 17280300.0 18457700 6
243 47354800.0 50583400 6
[2 rows x 58 columns]
Outliers in p10u_600:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
101 CHINA 36.708333 121.383333 742392000 820894000 873598000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
101 431762000.0 476751000.0 507009000.0 310629000 ... 36147200.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
101 39052100.0 41715800.0 20213900.0 21771800.0 23258100.0 15933300.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
101 17280300.0 18457700 6
243 47354800.0 50583400 6
[2 rows x 58 columns]
Outliers in p90r_600:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
101 CHINA 36.708333 121.383333 742392000 820894000 873598000
153 INDIA 12.553056 80.173333 356322000 421981000 489751000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
101 431762000.0 476751000.0 507009000.0 310629000 ... 36147200.0
153 115092000.0 137358000.0 159491000.0 241229000 ... 48114200.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
101 39052100.0 41715800.0 20213900.0 21771800.0 23258100.0 15933300.0
153 55720800.0 64848800.0 19946200.0 23014700.0 26796200.0 28168000.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
101 17280300.0 18457700 6
153 32706100.0 38052600 12
167 103177000.0 120268000 12
204 48117100.0 55996900 12
243 47354800.0 50583400 6
[5 rows x 58 columns]
Outliers in p00r_600:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
43 PAKISTAN 32.390929 71.463095 424183000 546171000 651723000
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
153 INDIA 12.553056 80.173333 356322000 421981000 489751000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
43 131066000.0 169058000.0 200907000.0 293117000 ... 70198600.0
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
153 115092000.0 137358000.0 159491000.0 241229000 ... 48114200.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
43 89264100.0 111771000.0 21823700.0 27781300.0 34699400.0 48374900.0
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
153 55720800.0 64848800.0 19946200.0 23014700.0 26796200.0 28168000.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
43 61482800.0 77071200 21
123 48695900.0 56664800 12
153 32706100.0 38052600 12
167 103177000.0 120268000 12
204 48117100.0 55996900 12
243 47354800.0 50583400 6
[6 rows x 58 columns]
Outliers in p10r_600:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
43 PAKISTAN 32.390929 71.463095 424183000 546171000 651723000
122 INDIA 14.865026 74.438709 411477000 487089000 565595000
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
153 INDIA 12.553056 80.173333 356322000 421981000 489751000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
43 131066000.0 169058000.0 200907000.0 293117000 ... 70198600.0
122 137096000.0 163614000.0 190060000.0 274382000 ... 35497700.0
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
153 115092000.0 137358000.0 159491000.0 241229000 ... 48114200.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
43 89264100.0 111771000.0 21823700.0 27781300.0 34699400.0 48374900.0
122 43529800.0 50642200.0 9658560.0 11757800.0 13680800.0 25839200.0
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
153 55720800.0 64848800.0 19946200.0 23014700.0 26796200.0 28168000.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
43 61482800.0 77071200 21
122 31772000.0 36961400 12
123 48695900.0 56664800 12
153 32706100.0 38052600 12
167 103177000.0 120268000 12
204 48117100.0 55996900 12
243 47354800.0 50583400 6
[7 rows x 58 columns]
Outliers in p90_300:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
167 103177000.0 120268000 12
243 47354800.0 50583400 6
[2 rows x 58 columns]
Outliers in p00_300:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
167 103177000.0 120268000 12
201 37271400.0 39811200 6
243 47354800.0 50583400 6
[3 rows x 58 columns]
Outliers in p10_300:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
201 CHINA 30.435681 120.947735 808445000 906681000 966557000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
201 485750000.0 546473000.0 582459000.0 322695000 ... 93688800.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
201 105361000.0 112549000.0 60547000.0 68089300.0 72737400.0 33141900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
167 103177000.0 120268000 12
201 37271400.0 39811200 6
243 47354800.0 50583400 6
[3 rows x 58 columns]
Outliers in p90u_300: Empty DataFrame Columns: [Country, Latitude, Longitude, p90_1200, p00_1200, p10_1200, p90u_1200, p00u_1200, p10u_1200, p90r_1200, p00r_1200, p10r_1200, p90_30, p00_30, p10_30, p90u_30, p00u_30, p10u_30, p90r_30, p00r_30, p10r_30, p90_75, p00_75, p10_75, p90u_75, p00u_75, p10u_75, p90r_75, p00r_75, p10r_75, p90_150, p00_150, p10_150, p90u_150, p00u_150, p10u_150, p90r_150, p00r_150, p10r_150, p90_600, p00_600, p10_600, p90u_600, p00u_600, p10u_600, p90r_600, p00r_600, p10r_600, p90_300, p00_300, p10_300, p90u_300, p00u_300, p10u_300, p90r_300, p00r_300, p10r_300, Country_encoded] Index: [] [0 rows x 58 columns]
Outliers in p00u_300: Empty DataFrame Columns: [Country, Latitude, Longitude, p90_1200, p00_1200, p10_1200, p90u_1200, p00u_1200, p10u_1200, p90r_1200, p00r_1200, p10r_1200, p90_30, p00_30, p10_30, p90u_30, p00u_30, p10u_30, p90r_30, p00r_30, p10r_30, p90_75, p00_75, p10_75, p90u_75, p00u_75, p10u_75, p90r_75, p00r_75, p10r_75, p90_150, p00_150, p10_150, p90u_150, p00u_150, p10u_150, p90r_150, p00r_150, p10r_150, p90_600, p00_600, p10_600, p90u_600, p00u_600, p10u_600, p90r_600, p00r_600, p10r_600, p90_300, p00_300, p10_300, p90u_300, p00u_300, p10u_300, p90r_300, p00r_300, p10r_300, Country_encoded] Index: [] [0 rows x 58 columns]
Outliers in p10u_300: Empty DataFrame Columns: [Country, Latitude, Longitude, p90_1200, p00_1200, p10_1200, p90u_1200, p00u_1200, p10u_1200, p90r_1200, p00r_1200, p10r_1200, p90_30, p00_30, p10_30, p90u_30, p00u_30, p10u_30, p90r_30, p00r_30, p10r_30, p90_75, p00_75, p10_75, p90u_75, p00u_75, p10u_75, p90r_75, p00r_75, p10r_75, p90_150, p00_150, p10_150, p90u_150, p00u_150, p10u_150, p90r_150, p00r_150, p10r_150, p90_600, p00_600, p10_600, p90u_600, p00u_600, p10u_600, p90r_600, p00r_600, p10r_600, p90_300, p00_300, p10_300, p90u_300, p00u_300, p10u_300, p90r_300, p00r_300, p10r_300, Country_encoded] Index: [] [0 rows x 58 columns]
Outliers in p90r_300:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
43 PAKISTAN 32.390929 71.463095 424183000 546171000 651723000
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
43 131066000.0 169058000.0 200907000.0 293117000 ... 70198600.0
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
43 89264100.0 111771000.0 21823700.0 27781300.0 34699400.0 48374900.0
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
43 61482800.0 77071200 21
123 48695900.0 56664800 12
167 103177000.0 120268000 12
204 48117100.0 55996900 12
243 47354800.0 50583400 6
[5 rows x 58 columns]
Outliers in p00r_300:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
43 PAKISTAN 32.390929 71.463095 424183000 546171000 651723000
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
43 131066000.0 169058000.0 200907000.0 293117000 ... 70198600.0
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
43 89264100.0 111771000.0 21823700.0 27781300.0 34699400.0 48374900.0
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
43 61482800.0 77071200 21
123 48695900.0 56664800 12
167 103177000.0 120268000 12
204 48117100.0 55996900 12
243 47354800.0 50583400 6
[5 rows x 58 columns]
Outliers in p10r_300:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 \
43 PAKISTAN 32.390929 71.463095 424183000 546171000 651723000
123 INDIA 21.236525 73.350193 658974000 816024000 958281000
167 INDIA 28.159867 78.408658 818999000 1007240000 1188970000
204 INDIA 24.872911 75.620019 780956000 957012000 1128870000
243 CHINA 34.687468 119.459851 826843000 914475000 974481000
p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
43 131066000.0 169058000.0 200907000.0 293117000 ... 70198600.0
123 204556000.0 256667000.0 301941000.0 454417000 ... 74280500.0
167 218150000.0 274801000.0 324681000.0 600849000 ... 115482000.0
204 214412000.0 269312000.0 317973000.0 566544000 ... 55657000.0
243 475506000.0 526671000.0 561015000.0 351337000 ... 106586000.0
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 \
43 89264100.0 111771000.0 21823700.0 27781300.0 34699400.0 48374900.0
123 93957100.0 109390000.0 35411200.0 45261200.0 52725300.0 38869300.0
167 153113000.0 178411000.0 36030100.0 49936000.0 58143700.0 79451800.0
204 64985200.0 75634600.0 14781100.0 16868100.0 19637700.0 40875900.0
243 115666000.0 123553000.0 62974600.0 68311400.0 72969700.0 43611600.0
p00r_300 p10r_300 Country_encoded
43 61482800.0 77071200 21
123 48695900.0 56664800 12
167 103177000.0 120268000 12
204 48117100.0 55996900 12
243 47354800.0 50583400 6
[5 rows x 58 columns]
# Select the columns you want to standardize
column_standardize = ['Country_encoded', 'Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300']
# Create a StandardScaler object
scaler = StandardScaler()
# Fit the scaler to the selected columns
scaler.fit(df[column_standardize])
# Transform the selected columns by applying standardization
df[column_standardize] = scaler.transform(df[column_standardize])
df[column_standardize]
| Country_encoded | Latitude | Longitude | p90_1200 | p00_1200 | p10_1200 | p90u_1200 | p00u_1200 | p10u_1200 | p90r_1200 | ... | p10r_600 | p90_300 | p00_300 | p10_300 | p90u_300 | p00u_300 | p10u_300 | p90r_300 | p00r_300 | p10r_300 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.605681 | 1.352765 | 0.251194 | -0.464402 | -0.508016 | -0.554848 | -0.593107 | -0.660152 | -0.734917 | -0.252291 | ... | -0.599107 | -1.172443 | -1.136296 | -1.125151 | -1.191514 | -1.198046 | -1.215751 | -0.738981 | -0.675829 | -0.636833 |
| 1 | 0.514631 | -0.126329 | -0.064588 | -0.774040 | -0.731805 | -0.679515 | -0.964721 | -0.953322 | -0.919337 | -0.444835 | ... | -0.317110 | -0.586885 | -0.600261 | -0.556108 | -0.716401 | -0.736024 | -0.708998 | -0.154835 | -0.193095 | -0.156917 |
| 2 | -1.670572 | -4.916056 | -0.579314 | -1.002907 | -0.916580 | -0.857975 | -1.183329 | -1.120005 | -1.079853 | -0.644420 | ... | -0.339928 | 0.414370 | 0.496365 | 0.605076 | 0.653952 | 0.779507 | 0.935261 | -0.156248 | -0.111894 | -0.068467 |
| 3 | 1.060932 | -0.469286 | -1.226995 | -0.889115 | -0.813688 | -0.764634 | -0.908713 | -0.841270 | -0.801030 | -0.714629 | ... | -0.584963 | -1.145335 | -1.094991 | -1.071174 | -1.169357 | -1.162423 | -1.166418 | -0.712229 | -0.638664 | -0.593155 |
| 4 | 0.514631 | -0.020197 | 0.018589 | 0.051419 | 0.029799 | 0.051246 | 0.176309 | 0.148003 | 0.179467 | -0.085040 | ... | -0.367127 | -0.741186 | -0.728155 | -0.685837 | -0.701982 | -0.714859 | -0.682794 | -0.559051 | -0.517095 | -0.473250 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 271 | -1.397422 | -1.506439 | 1.501828 | 1.792834 | 1.899641 | 1.913771 | 0.839405 | 1.051898 | 1.110167 | 2.454943 | ... | 1.483419 | 1.727114 | 2.251499 | 2.276462 | 1.196198 | 1.746700 | 1.828051 | 2.090747 | 2.335818 | 2.210780 |
| 272 | 1.060932 | 0.096301 | -0.957393 | -0.854920 | -0.820254 | -0.771264 | -0.789761 | -0.786267 | -0.743154 | -0.772925 | ... | -0.572764 | 0.245594 | 0.188036 | 0.253678 | 0.620326 | 0.569365 | 0.671899 | -0.509843 | -0.477951 | -0.437206 |
| 273 | -0.395870 | -0.461599 | 1.689799 | 2.357024 | 2.340021 | 2.270520 | 2.538004 | 2.634711 | 2.644594 | 1.762702 | ... | 0.120417 | 0.566912 | 0.570582 | 0.541617 | 0.867854 | 0.941671 | 0.931923 | -0.165667 | -0.201114 | -0.199622 |
| 274 | 0.878832 | 0.461080 | 0.470339 | 0.061055 | -0.037393 | -0.131383 | -0.141744 | -0.234249 | -0.340844 | 0.257537 | ... | -0.223553 | -0.455104 | -0.551699 | -0.640980 | -0.537094 | -0.643647 | -0.744355 | -0.153133 | -0.229645 | -0.287221 |
| 275 | 1.060932 | 0.074844 | -1.154910 | -0.616189 | -0.577300 | -0.524737 | -0.479518 | -0.452401 | -0.391292 | -0.648693 | ... | -0.535978 | -0.497452 | -0.464913 | -0.420298 | -0.360175 | -0.330085 | -0.282576 | -0.574150 | -0.530945 | -0.488370 |
276 rows × 57 columns
#correlation
df.corr()
C:\Users\prabha\AppData\Local\Temp\ipykernel_15380\930112212.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. df.corr()
| Latitude | Longitude | p90_1200 | p00_1200 | p10_1200 | p90u_1200 | p00u_1200 | p10u_1200 | p90r_1200 | p00r_1200 | ... | p90_300 | p00_300 | p10_300 | p90u_300 | p00u_300 | p10u_300 | p90r_300 | p00r_300 | p10r_300 | Country_encoded | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Latitude | 1.000000 | -0.042836 | -0.009781 | -0.073953 | -0.111928 | 0.156477 | 0.090043 | 0.053836 | -0.177858 | -0.215616 | ... | 0.012607 | -0.045970 | -0.078337 | 0.082410 | 0.031985 | 0.005220 | -0.116827 | -0.155200 | -0.175544 | 0.084602 |
| Longitude | -0.042836 | 1.000000 | 0.593553 | 0.587779 | 0.567162 | 0.503450 | 0.514429 | 0.498042 | 0.582437 | 0.560700 | ... | 0.448198 | 0.446263 | 0.426163 | 0.354557 | 0.361703 | 0.341938 | 0.463441 | 0.438351 | 0.414278 | -0.549427 |
| p90_1200 | -0.009781 | 0.593553 | 1.000000 | 0.993697 | 0.981456 | 0.915903 | 0.940142 | 0.947505 | 0.912134 | 0.881890 | ... | 0.683437 | 0.705045 | 0.703963 | 0.503745 | 0.537621 | 0.541605 | 0.772836 | 0.746307 | 0.718247 | -0.552493 |
| p00_1200 | -0.073953 | 0.587779 | 0.993697 | 1.000000 | 0.996458 | 0.874553 | 0.909984 | 0.924483 | 0.942714 | 0.921350 | ... | 0.671828 | 0.705215 | 0.712202 | 0.476288 | 0.518611 | 0.528324 | 0.793592 | 0.776905 | 0.755299 | -0.529692 |
| p10_1200 | -0.111928 | 0.567162 | 0.981456 | 0.996458 | 1.000000 | 0.838057 | 0.880295 | 0.900355 | 0.957368 | 0.942899 | ... | 0.662495 | 0.703799 | 0.717241 | 0.455220 | 0.502707 | 0.516957 | 0.808476 | 0.798964 | 0.782695 | -0.510042 |
| p90u_1200 | 0.156477 | 0.503450 | 0.915903 | 0.874553 | 0.838057 | 1.000000 | 0.994082 | 0.984650 | 0.670895 | 0.619903 | ... | 0.655884 | 0.636550 | 0.611634 | 0.584574 | 0.589668 | 0.579526 | 0.560367 | 0.508081 | 0.464968 | -0.518746 |
| p00u_1200 | 0.090043 | 0.514429 | 0.940142 | 0.909984 | 0.880295 | 0.994082 | 1.000000 | 0.997322 | 0.721719 | 0.677228 | ... | 0.660659 | 0.652715 | 0.634439 | 0.573294 | 0.587676 | 0.582786 | 0.592297 | 0.547947 | 0.509093 | -0.510720 |
| p10u_1200 | 0.053836 | 0.498042 | 0.947505 | 0.924483 | 0.900355 | 0.984650 | 0.997322 | 1.000000 | 0.744952 | 0.705466 | ... | 0.662244 | 0.661272 | 0.648351 | 0.565323 | 0.585009 | 0.584451 | 0.610473 | 0.571614 | 0.536482 | -0.499197 |
| p90r_1200 | -0.177858 | 0.582437 | 0.912134 | 0.942714 | 0.957368 | 0.670895 | 0.721719 | 0.744952 | 1.000000 | 0.996198 | ... | 0.592830 | 0.652492 | 0.675936 | 0.333685 | 0.391065 | 0.408782 | 0.855526 | 0.859906 | 0.852093 | -0.490963 |
| p00r_1200 | -0.215616 | 0.560700 | 0.881890 | 0.921350 | 0.942899 | 0.619903 | 0.677228 | 0.705466 | 0.996198 | 1.000000 | ... | 0.572753 | 0.639449 | 0.668980 | 0.307685 | 0.369304 | 0.391125 | 0.852904 | 0.864876 | 0.862962 | -0.461099 |
| p10r_1200 | -0.238073 | 0.535896 | 0.852854 | 0.898521 | 0.925566 | 0.575487 | 0.636644 | 0.668593 | 0.987911 | 0.997540 | ... | 0.555958 | 0.627389 | 0.661602 | 0.286079 | 0.350105 | 0.374943 | 0.850451 | 0.868009 | 0.870772 | -0.437276 |
| p90_30 | -0.042807 | 0.278141 | 0.390220 | 0.398820 | 0.397571 | 0.361934 | 0.383573 | 0.390973 | 0.351306 | 0.348093 | ... | 0.342201 | 0.353131 | 0.350017 | 0.314624 | 0.334661 | 0.337283 | 0.275103 | 0.269882 | 0.257849 | -0.156847 |
| p00_30 | -0.071382 | 0.272715 | 0.395847 | 0.410268 | 0.413002 | 0.349772 | 0.376796 | 0.387706 | 0.374122 | 0.374761 | ... | 0.337559 | 0.356699 | 0.357316 | 0.300235 | 0.327166 | 0.332768 | 0.289517 | 0.289894 | 0.280089 | -0.153498 |
| p10_30 | -0.091969 | 0.261762 | 0.385404 | 0.404440 | 0.410763 | 0.327720 | 0.358283 | 0.371956 | 0.377348 | 0.381776 | ... | 0.319007 | 0.342038 | 0.346256 | 0.277772 | 0.307134 | 0.315436 | 0.284293 | 0.288444 | 0.281686 | -0.139111 |
| p90u_30 | -0.025076 | 0.202648 | 0.262994 | 0.269027 | 0.266086 | 0.263968 | 0.280668 | 0.285346 | 0.216306 | 0.214256 | ... | 0.211601 | 0.212181 | 0.206587 | 0.225596 | 0.235222 | 0.235816 | 0.114451 | 0.107904 | 0.098541 | -0.073771 |
| p00u_30 | -0.048304 | 0.197067 | 0.265332 | 0.275863 | 0.275960 | 0.253946 | 0.274822 | 0.282248 | 0.230859 | 0.231867 | ... | 0.204362 | 0.210474 | 0.207392 | 0.212824 | 0.227324 | 0.230066 | 0.119596 | 0.116580 | 0.108661 | -0.070419 |
| p10u_30 | -0.065892 | 0.187799 | 0.255588 | 0.269769 | 0.272688 | 0.235632 | 0.259306 | 0.268926 | 0.231560 | 0.235601 | ... | 0.187099 | 0.196002 | 0.195585 | 0.193482 | 0.209781 | 0.214621 | 0.111940 | 0.111605 | 0.105901 | -0.059179 |
| p90r_30 | -0.091113 | 0.421084 | 0.682476 | 0.696501 | 0.703346 | 0.546776 | 0.575516 | 0.589791 | 0.702471 | 0.696355 | ... | 0.680388 | 0.728709 | 0.738298 | 0.491953 | 0.543683 | 0.553320 | 0.786499 | 0.790399 | 0.774745 | -0.411759 |
| p00r_30 | -0.117985 | 0.400524 | 0.664460 | 0.685194 | 0.696747 | 0.508891 | 0.543169 | 0.561031 | 0.707875 | 0.706620 | ... | 0.654482 | 0.713562 | 0.728623 | 0.457503 | 0.516960 | 0.530431 | 0.784732 | 0.798480 | 0.787424 | -0.387928 |
| p10r_30 | -0.136154 | 0.386479 | 0.653287 | 0.679280 | 0.695131 | 0.481798 | 0.519890 | 0.540839 | 0.714900 | 0.717950 | ... | 0.639250 | 0.703954 | 0.724050 | 0.433494 | 0.496047 | 0.512797 | 0.790424 | 0.809901 | 0.803366 | -0.369033 |
| p90_75 | -0.089833 | 0.290413 | 0.494629 | 0.504149 | 0.505120 | 0.440615 | 0.466112 | 0.477876 | 0.463846 | 0.457601 | ... | 0.588741 | 0.613657 | 0.613717 | 0.545132 | 0.584054 | 0.594746 | 0.466427 | 0.465026 | 0.447212 | -0.257120 |
| p00_75 | -0.125558 | 0.282451 | 0.497924 | 0.514842 | 0.520680 | 0.419051 | 0.451512 | 0.467409 | 0.491956 | 0.490267 | ... | 0.568476 | 0.605787 | 0.610878 | 0.508644 | 0.557429 | 0.571629 | 0.482146 | 0.489473 | 0.474878 | -0.250862 |
| p10_75 | -0.149335 | 0.269202 | 0.490433 | 0.512592 | 0.522592 | 0.396990 | 0.433616 | 0.452825 | 0.500642 | 0.503052 | ... | 0.552840 | 0.595212 | 0.604845 | 0.485971 | 0.537956 | 0.555543 | 0.484449 | 0.496410 | 0.485430 | -0.233543 |
| p90u_75 | -0.069933 | 0.193839 | 0.348977 | 0.355197 | 0.353557 | 0.335294 | 0.355842 | 0.364899 | 0.302318 | 0.296675 | ... | 0.449885 | 0.464933 | 0.461427 | 0.469045 | 0.499679 | 0.509164 | 0.262329 | 0.261460 | 0.245716 | -0.159453 |
| p00u_75 | -0.101832 | 0.189903 | 0.354118 | 0.366118 | 0.368009 | 0.321228 | 0.347575 | 0.359835 | 0.326178 | 0.323806 | ... | 0.431643 | 0.456767 | 0.456650 | 0.438503 | 0.477678 | 0.489641 | 0.272349 | 0.277886 | 0.263982 | -0.158670 |
| p10u_75 | -0.122215 | 0.177409 | 0.345034 | 0.361059 | 0.366094 | 0.302142 | 0.331730 | 0.346565 | 0.328885 | 0.329683 | ... | 0.414566 | 0.443150 | 0.446317 | 0.418013 | 0.459435 | 0.474019 | 0.267207 | 0.275960 | 0.264639 | -0.142690 |
| p90r_75 | -0.107191 | 0.448397 | 0.703960 | 0.719068 | 0.727788 | 0.549890 | 0.578077 | 0.592412 | 0.738981 | 0.733992 | ... | 0.728898 | 0.772337 | 0.783635 | 0.509052 | 0.554405 | 0.563481 | 0.874802 | 0.872412 | 0.857091 | -0.434870 |
| p00r_75 | -0.134632 | 0.421512 | 0.684287 | 0.707621 | 0.722479 | 0.505640 | 0.540367 | 0.559310 | 0.747822 | 0.749038 | ... | 0.698500 | 0.754025 | 0.772329 | 0.466688 | 0.520151 | 0.534016 | 0.876208 | 0.885273 | 0.875878 | -0.404121 |
| p10r_75 | -0.152810 | 0.402159 | 0.668005 | 0.696889 | 0.716396 | 0.473309 | 0.511908 | 0.534159 | 0.750758 | 0.756674 | ... | 0.677438 | 0.738747 | 0.762515 | 0.437371 | 0.493851 | 0.511275 | 0.877116 | 0.892386 | 0.888040 | -0.381498 |
| p90_150 | -0.078705 | 0.383489 | 0.498071 | 0.499282 | 0.496760 | 0.457713 | 0.471595 | 0.476610 | 0.452746 | 0.443829 | ... | 0.808004 | 0.813821 | 0.805575 | 0.773159 | 0.796794 | 0.800752 | 0.595307 | 0.581380 | 0.557702 | -0.358007 |
| p00_150 | -0.121544 | 0.375054 | 0.520748 | 0.531776 | 0.536025 | 0.443795 | 0.467185 | 0.478155 | 0.508852 | 0.505629 | ... | 0.796680 | 0.820111 | 0.819651 | 0.733034 | 0.770332 | 0.779727 | 0.639474 | 0.637715 | 0.618573 | -0.352651 |
| p10_150 | -0.147272 | 0.355924 | 0.522420 | 0.539637 | 0.548932 | 0.426676 | 0.455277 | 0.470538 | 0.529421 | 0.530741 | ... | 0.786982 | 0.817871 | 0.823582 | 0.710949 | 0.753224 | 0.767324 | 0.655283 | 0.659817 | 0.645107 | -0.334995 |
| p90u_150 | -0.050610 | 0.295709 | 0.328158 | 0.322101 | 0.313866 | 0.351819 | 0.356897 | 0.356464 | 0.246981 | 0.236963 | ... | 0.691987 | 0.679992 | 0.663645 | 0.746001 | 0.755851 | 0.756108 | 0.359498 | 0.342604 | 0.318644 | -0.256568 |
| p00u_150 | -0.089724 | 0.294465 | 0.357470 | 0.359223 | 0.356044 | 0.351899 | 0.365227 | 0.369677 | 0.301051 | 0.295030 | ... | 0.695414 | 0.698221 | 0.687508 | 0.726595 | 0.748819 | 0.753435 | 0.402693 | 0.395166 | 0.373700 | -0.260056 |
| p10u_150 | -0.112548 | 0.275048 | 0.359229 | 0.365703 | 0.366302 | 0.341098 | 0.358716 | 0.366706 | 0.315330 | 0.312632 | ... | 0.689454 | 0.697963 | 0.691899 | 0.714137 | 0.740600 | 0.749300 | 0.410411 | 0.407643 | 0.389150 | -0.244000 |
| p90r_150 | -0.117169 | 0.445536 | 0.721368 | 0.740618 | 0.753745 | 0.534640 | 0.565129 | 0.581931 | 0.786713 | 0.784373 | ... | 0.762769 | 0.811600 | 0.827516 | 0.515817 | 0.564655 | 0.576388 | 0.945732 | 0.945264 | 0.932310 | -0.465686 |
| p00r_150 | -0.147858 | 0.416005 | 0.695123 | 0.723150 | 0.742765 | 0.484330 | 0.521791 | 0.543482 | 0.789603 | 0.794011 | ... | 0.723758 | 0.785402 | 0.808567 | 0.467564 | 0.525080 | 0.541814 | 0.936575 | 0.948680 | 0.942170 | -0.429625 |
| p10r_150 | -0.165813 | 0.394212 | 0.673938 | 0.707648 | 0.732051 | 0.448201 | 0.489486 | 0.514540 | 0.787358 | 0.796789 | ... | 0.697431 | 0.764559 | 0.793337 | 0.434420 | 0.494539 | 0.514863 | 0.931435 | 0.949895 | 0.948872 | -0.402909 |
| p90_600 | 0.076954 | 0.445624 | 0.843624 | 0.820751 | 0.804664 | 0.822802 | 0.822563 | 0.822873 | 0.718312 | 0.685215 | ... | 0.865318 | 0.854707 | 0.841665 | 0.735396 | 0.745268 | 0.742552 | 0.803555 | 0.756093 | 0.720038 | -0.559132 |
| p00_600 | 0.014709 | 0.451663 | 0.863248 | 0.852601 | 0.844762 | 0.802299 | 0.813779 | 0.821253 | 0.775502 | 0.749968 | ... | 0.863481 | 0.867144 | 0.862588 | 0.709083 | 0.729632 | 0.732838 | 0.846222 | 0.809181 | 0.779070 | -0.551341 |
| p10_600 | -0.020343 | 0.433592 | 0.859790 | 0.857548 | 0.856500 | 0.774521 | 0.792916 | 0.805978 | 0.797480 | 0.778304 | ... | 0.854128 | 0.866162 | 0.868737 | 0.685258 | 0.711227 | 0.719475 | 0.865996 | 0.836200 | 0.811760 | -0.532534 |
| p90u_600 | 0.198119 | 0.342015 | 0.681404 | 0.633832 | 0.600762 | 0.801413 | 0.777294 | 0.764457 | 0.440467 | 0.395981 | ... | 0.828019 | 0.782108 | 0.748807 | 0.823624 | 0.808715 | 0.795026 | 0.553918 | 0.490431 | 0.444374 | -0.485486 |
| p00u_600 | 0.147573 | 0.354976 | 0.718142 | 0.678759 | 0.650654 | 0.812509 | 0.797710 | 0.790220 | 0.497006 | 0.456562 | ... | 0.845781 | 0.810137 | 0.782014 | 0.824353 | 0.818288 | 0.808881 | 0.596168 | 0.538855 | 0.495329 | -0.491781 |
| p10u_600 | 0.121725 | 0.335228 | 0.724191 | 0.690346 | 0.666795 | 0.805892 | 0.796343 | 0.793384 | 0.514937 | 0.478404 | ... | 0.848104 | 0.817994 | 0.794808 | 0.819094 | 0.817201 | 0.812011 | 0.611291 | 0.558423 | 0.518183 | -0.479169 |
| p90r_600 | -0.116411 | 0.474364 | 0.849645 | 0.868109 | 0.880540 | 0.633530 | 0.667412 | 0.686391 | 0.922714 | 0.914701 | ... | 0.687381 | 0.729949 | 0.749285 | 0.413130 | 0.455710 | 0.469367 | 0.944956 | 0.933026 | 0.920867 | -0.514817 |
| p00r_600 | -0.158411 | 0.458346 | 0.825450 | 0.854177 | 0.874190 | 0.582275 | 0.624287 | 0.648904 | 0.930355 | 0.930413 | ... | 0.663231 | 0.716049 | 0.742704 | 0.379200 | 0.428367 | 0.446821 | 0.946566 | 0.944921 | 0.939678 | -0.484983 |
| p10r_600 | -0.182044 | 0.437295 | 0.797284 | 0.832764 | 0.858436 | 0.537779 | 0.584269 | 0.612792 | 0.923757 | 0.929932 | ... | 0.640775 | 0.699237 | 0.731425 | 0.351110 | 0.403125 | 0.425036 | 0.941858 | 0.946870 | 0.947306 | -0.457815 |
| p90_300 | 0.012607 | 0.448198 | 0.683437 | 0.671828 | 0.662495 | 0.655884 | 0.660659 | 0.662244 | 0.592830 | 0.572753 | ... | 1.000000 | 0.990770 | 0.977804 | 0.935726 | 0.950012 | 0.950329 | 0.774680 | 0.739618 | 0.708504 | -0.456634 |
| p00_300 | -0.045970 | 0.446263 | 0.705045 | 0.705215 | 0.703799 | 0.636550 | 0.652715 | 0.661272 | 0.652492 | 0.639449 | ... | 0.990770 | 1.000000 | 0.996047 | 0.897112 | 0.925717 | 0.932376 | 0.821271 | 0.799186 | 0.773820 | -0.451601 |
| p10_300 | -0.078337 | 0.426163 | 0.703963 | 0.712202 | 0.717241 | 0.611634 | 0.634439 | 0.648351 | 0.675936 | 0.668980 | ... | 0.977804 | 0.996047 | 1.000000 | 0.868113 | 0.902519 | 0.914888 | 0.841464 | 0.827076 | 0.807827 | -0.430609 |
| p90u_300 | 0.082410 | 0.354557 | 0.503745 | 0.476288 | 0.455220 | 0.584574 | 0.573294 | 0.565323 | 0.333685 | 0.307685 | ... | 0.935726 | 0.897112 | 0.868113 | 1.000000 | 0.993769 | 0.986514 | 0.501838 | 0.457438 | 0.420541 | -0.353348 |
| p00u_300 | 0.031985 | 0.361703 | 0.537621 | 0.518611 | 0.502707 | 0.589668 | 0.587676 | 0.585009 | 0.391065 | 0.369304 | ... | 0.950012 | 0.925717 | 0.902519 | 0.993769 | 1.000000 | 0.997512 | 0.548043 | 0.512479 | 0.478237 | -0.360308 |
| p10u_300 | 0.005220 | 0.341938 | 0.541605 | 0.528324 | 0.516957 | 0.579526 | 0.582786 | 0.584451 | 0.408782 | 0.391125 | ... | 0.950329 | 0.932376 | 0.914888 | 0.986514 | 0.997512 | 1.000000 | 0.561826 | 0.531552 | 0.501118 | -0.342843 |
| p90r_300 | -0.116827 | 0.463441 | 0.772836 | 0.793592 | 0.808476 | 0.560367 | 0.592297 | 0.610473 | 0.855526 | 0.852904 | ... | 0.774680 | 0.821271 | 0.841464 | 0.501838 | 0.548043 | 0.561826 | 1.000000 | 0.993620 | 0.983469 | -0.486294 |
| p00r_300 | -0.155200 | 0.438351 | 0.746307 | 0.776905 | 0.798964 | 0.508081 | 0.547947 | 0.571614 | 0.859906 | 0.864876 | ... | 0.739618 | 0.799186 | 0.827076 | 0.457438 | 0.512479 | 0.531552 | 0.993620 | 1.000000 | 0.996828 | -0.452686 |
| p10r_300 | -0.175544 | 0.414278 | 0.718247 | 0.755299 | 0.782695 | 0.464968 | 0.509093 | 0.536482 | 0.852093 | 0.862962 | ... | 0.708504 | 0.773820 | 0.807827 | 0.420541 | 0.478237 | 0.501118 | 0.983469 | 0.996828 | 1.000000 | -0.422487 |
| Country_encoded | 0.084602 | -0.549427 | -0.552493 | -0.529692 | -0.510042 | -0.518746 | -0.510720 | -0.499197 | -0.490963 | -0.461099 | ... | -0.456634 | -0.451601 | -0.430609 | -0.353348 | -0.360308 | -0.342843 | -0.486294 | -0.452686 | -0.422487 | 1.000000 |
57 rows × 57 columns
# Step 2: Subset the data for relevant features
independent_features = ['p90_1200', 'p90u_1200', 'p90r_1200', 'p90_30', 'p90u_30', 'p90r_30', 'p90_75',
'p90u_75', 'p90r_75', 'p90_150', 'p90u_150', 'p90r_150', 'p90_600', 'p90u_600',
'p90r_600', 'p90_300', 'p90u_300', 'p90r_300']
dependent_features = ['p00_1200', 'p10_1200', 'p00u_1200', 'p10u_1200', 'p00r_1200', 'p10r_1200', 'p00_30',
'p10_30', 'p00u_30', 'p10u_30', 'p00r_30', 'p10r_30', 'p00_75', 'p10_75', 'p00u_75',
'p10u_75', 'p00r_75', 'p10r_75', 'p00_150', 'p10_150', 'p00u_150', 'p10u_150', 'p00r_150',
'p10r_150', 'p00_600', 'p10_600', 'p00u_600', 'p10u_600', 'p00r_600', 'p10r_600', 'p00_300',
'p10_300', 'p00u_300', 'p10u_300', 'p00r_300', 'p10r_300']
relevant_columns = independent_features + dependent_features
relevant_data = df[relevant_columns]
# Step 3: Data visualization
# Example 1: Box plot comparing independent features
plt.figure(figsize=(20, 12))
sns.boxplot(data=relevant_data[independent_features])
plt.title('Population Distribution - Independent Features')
plt.xticks(rotation=90)
plt.show()
# Compute the correlation matrix
correlation_matrix = df.corr()
# Set the dark background style
sns.set_style('dark')
# Create the heat map
plt.figure(figsize=(40,32))
sns.heatmap(correlation_matrix, cmap='coolwarm', annot=True, fmt='.2f')
plt.title('Correlation Heat Map')
plt.show()
C:\Users\prabha\AppData\Local\Temp\ipykernel_15380\3568341587.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. correlation_matrix = df.corr()
import pandas as pd
# Print the first few rows of the DataFrame
print(df.head())
# List the column names of the DataFrame
print("Column names:", df.columns)
# Check the data types of the columns
print("Data types:", df.dtypes)
# Find the unique values in each column
for column in df.columns:
unique_values = df[column].unique()
print(f"Unique values in {column}:", unique_values)
Country Latitude Longitude p90_1200 p00_1200 \
0 SWEDEN 1.352765 0.251194 -0.464402 -0.508016
1 SPAIN -0.126329 -0.064588 -0.774040 -0.731805
2 BRAZIL -4.916056 -0.579314 -1.002907 -0.916580
3 UNITED STATES OF AMERICA -0.469286 -1.226995 -0.889115 -0.813688
4 SPAIN -0.020197 0.018589 0.051419 0.029799
p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 \
0 -0.554848 -0.593107 -0.660152 -0.734917 -0.252291 ... -1.172443
1 -0.679515 -0.964721 -0.953322 -0.919337 -0.444835 ... -0.586885
2 -0.857975 -1.183329 -1.120005 -1.079853 -0.644420 ... 0.414370
3 -0.764634 -0.908713 -0.841270 -0.801030 -0.714629 ... -1.145335
4 0.051246 0.176309 0.148003 0.179467 -0.085040 ... -0.741186
p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 \
0 -1.136296 -1.125151 -1.191514 -1.198046 -1.215751 -0.738981 -0.675829
1 -0.600261 -0.556108 -0.716401 -0.736024 -0.708998 -0.154835 -0.193095
2 0.496365 0.605076 0.653952 0.779507 0.935261 -0.156248 -0.111894
3 -1.094991 -1.071174 -1.169357 -1.162423 -1.166418 -0.712229 -0.638664
4 -0.728155 -0.685837 -0.701982 -0.714859 -0.682794 -0.559051 -0.517095
p10r_300 Country_encoded
0 -0.636833 0.605681
1 -0.156917 0.514631
2 -0.068467 -1.670572
3 -0.593155 1.060932
4 -0.473250 0.514631
[5 rows x 58 columns]
Column names: Index(['Country', 'Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200',
'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200',
'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30',
'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75',
'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75',
'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150',
'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600',
'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600',
'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300',
'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300', 'Country_encoded'],
dtype='object')
Data types: Country object
Latitude float64
Longitude float64
p90_1200 float64
p00_1200 float64
p10_1200 float64
p90u_1200 float64
p00u_1200 float64
p10u_1200 float64
p90r_1200 float64
p00r_1200 float64
p10r_1200 float64
p90_30 float64
p00_30 float64
p10_30 float64
p90u_30 float64
p00u_30 float64
p10u_30 float64
p90r_30 float64
p00r_30 float64
p10r_30 float64
p90_75 float64
p00_75 float64
p10_75 float64
p90u_75 float64
p00u_75 float64
p10u_75 float64
p90r_75 float64
p00r_75 float64
p10r_75 float64
p90_150 float64
p00_150 float64
p10_150 float64
p90u_150 float64
p00u_150 float64
p10u_150 float64
p90r_150 float64
p00r_150 float64
p10r_150 float64
p90_600 float64
p00_600 float64
p10_600 float64
p90u_600 float64
p00u_600 float64
p10u_600 float64
p90r_600 float64
p00r_600 float64
p10r_600 float64
p90_300 float64
p00_300 float64
p10_300 float64
p90u_300 float64
p00u_300 float64
p10u_300 float64
p90r_300 float64
p00r_300 float64
p10r_300 float64
Country_encoded float64
dtype: object
Unique values in Country: ['SWEDEN' 'SPAIN' 'BRAZIL' 'UNITED STATES OF AMERICA' 'ARGENTINA'
'GERMANY' 'RUSSIAN FEDERATION' 'BULGARIA' 'FRANCE' 'UNITED KINGDOM'
'SWITZERLAND' 'KAZAKHSTAN' 'SLOVAK REPUBLIC' 'NETHERLANDS' 'CANADA'
'IRAN, ISLAMIC REPUBLIC OF' 'ITALY' 'CHINA' 'ROMANIA' 'PAKISTAN'
'UKRAINE' 'TAIWAN, CHINA' 'BELGIUM' 'CZECH REPUBLIC' 'JAPAN' 'LITHUANIA'
'INDIA' 'SOUTH AFRICA' 'KOREA, REPUBLIC OF' 'SLOVENIA' 'MEXICO' 'FINLAND'
'ARMENIA' 'HUNGARY']
Unique values in Latitude: [ 1.35276534e+00 -1.26329198e-01 -4.91605559e+00 -4.69285615e-01
-2.01966081e-02 -5.75176834e+00 7.19662906e-01 8.10391363e-01
1.08881949e+00 -6.42768222e-02 1.65048566e-01 4.60751238e-01
1.17311830e+00 7.79858133e-01 4.64155655e-01 6.28639209e-01
2.96919319e-01 2.02717020e+00 2.89066925e-01 1.63337513e-01
5.36005870e-01 -1.76126296e+00 7.59966221e-01 7.83585927e-01
-1.68721014e-02 5.25243619e-01 9.44431357e-01 -5.15502359e-01
2.18171743e-01 9.47544955e-01 -5.72377348e-01 3.30218600e-01
-9.63498625e-01 4.65222766e-02 -2.06376609e-01 -2.31051159e-01
2.75048563e-01 -4.89028305e-01 6.06091657e-01 -1.31450997e-01
2.17869259e-01 -1.68069194e+00 1.03324959e+00 -6.91889629e-01
7.56758377e-01 -1.23362219e+00 4.39641307e-01 6.57667261e-01
3.80576565e-01 -9.85893915e-02 -1.71423539e-01 3.81726417e-01
-6.98990729e-01 -8.41018622e-02 3.27376985e-01 2.41448170e-01
-9.53711994e-01 -5.49182052e-01 4.78014273e-01 1.83335488e-01
1.00899530e-02 -4.76805405e-01 7.95658842e-01 7.51857399e-01
3.89811364e-02 2.18212232e-01 1.30488924e+00 -5.73195393e-03
4.84742806e-02 5.81178902e-01 7.20488924e-01 2.92060888e-01
-5.61940966e+00 8.39465242e-01 3.80051344e-02 -1.51353315e+00
-7.80959645e-01 4.91167425e-01 1.57001125e-01 6.15409426e-01
1.44401893e+00 4.26444126e-03 -9.30579003e-02 -4.35421092e-01
-3.08297140e-01 -3.16334745e-01 -1.22128476e+00 -1.57500804e-02
-6.06049417e-01 3.75893204e-01 4.49945143e-01 2.01400064e-01
6.49591302e-01 -7.21126149e-01 7.28282529e-01 9.66525067e-01
8.05973811e-01 -1.43866724e+00 5.37702894e-01 -5.38293530e-01
1.30166527e-03 -3.62686909e-01 -6.90538943e-02 -5.21855750e-01
1.00421079e+00 -7.26715285e-01 6.55009562e-01 9.58087302e-01
-2.11073400e-02 7.42971282e-01 7.42993776e-01 -1.26798593e-01
-1.52277125e-01 -5.51708763e-02 1.08697271e+00 1.08712506e+00
1.07814447e+00 -6.07987009e-01 -1.47523472e-02 5.44400097e-01
-8.50727554e-02 -3.81739687e-01 -2.02824243e+00 -1.54241465e+00
1.25358128e+00 -1.26724172e+00 -3.07549050e-01 2.19465251e-01
6.73867521e-01 5.82179609e-01 -5.72951473e+00 1.98265178e+00
-4.68450752e-01 1.73679092e-01 3.41075173e-01 9.10817772e-01
-2.53882391e+00 -1.23999144e+00 7.78658870e-01 1.59758483e-01
-1.65803177e+00 -1.68492264e-02 -3.01821095e-03 4.67904570e-01
1.40156536e+00 1.40038189e+00 -9.44384094e-02 -1.43798358e+00
8.40076463e-01 1.44156322e+00 3.98628560e-01 -1.25250501e+00
-1.48738248e+00 -2.20453050e+00 1.89798070e-01 2.04232986e-01
-4.59992246e-01 -9.76977222e-02 -4.39379542e-01 -1.19158414e-02
5.18598418e-01 7.44090711e-01 -4.36501252e-01 2.94968153e-01
4.19692742e-01 6.81919080e-01 5.82503443e-01 -1.01450876e+00
5.77737961e-01 5.44407417e-01 1.56871500e-01 -1.09951754e+00
5.37741019e-01 -2.59561092e-01 7.48656570e-01 7.48023694e-01
1.03847326e+00 6.02343504e-01 -5.08830470e-01 -4.51691883e-01
3.41974205e-03 7.76516165e-01 1.50757778e+00 -2.33697039e-01
1.21618004e+00 -1.25864072e-01 3.89457344e-01 6.53713147e-02
-6.15733186e-01 6.39984850e-01 1.63079101e-01 -1.30080705e-01
6.48944243e-01 2.56373594e-02 5.93829870e-01 1.78849539e-01
3.65640065e-02 -1.01607601e-01 2.14714866e-01 2.74791905e-01
2.40533168e-01 -8.40977590e-01 1.99185979e-02 1.38224753e-01
-1.26513966e+00 -2.37869600e-01 8.90763372e-01 1.20439162e+00
-8.16461717e-01 3.60037519e-01 7.52071509e-01 4.76954396e-01
-1.52658376e-01 -6.17212439e-01 -9.42738758e-01 9.98525493e-02
-9.43922780e-02 1.09283778e-01 9.87868553e-01 -7.34390473e-01
-4.75920904e-01 -4.44658341e-01 -3.35873008e-01 -4.51804276e-01
-4.37914243e-01 -6.43369836e-02 -3.84119312e-02 8.19698838e-01
9.68516264e-01 -9.66079769e-01 4.83982449e-01 3.00435289e-01
4.76977271e-01 -1.07636799e+00 9.26830305e-01 -3.27820230e-01
-2.84544998e-02 -1.49046901e+00 -4.53102054e-01 -1.64975504e+00
5.87983237e-01 -1.00022894e-01 7.78841490e-01 -5.16778176e-01
6.91474597e-01 -3.81117257e-01 1.19792575e-01 1.10589792e+00
8.73826078e-01 2.18443499e-01 -5.82863051e-02 3.48727028e-01
-4.35782823e-01 -1.22228029e+00 -3.33354084e-01 9.12167400e-01
6.55312580e-01 -2.93685552e-01 -3.91832015e-02 1.00301510e-01
1.40807469e+00 -5.46642922e-01 -6.34627974e-01 4.67754205e-01
-8.74579584e-01 -4.46983970e-01 7.02807352e-01 -2.45980939e-01
-4.38698400e-01 7.75791483e-01 9.11328648e-01 -1.50643922e+00
9.63013509e-02 -4.61598837e-01 4.61079800e-01 7.48437188e-02]
Unique values in Longitude: [ 2.51194074e-01 -6.45883067e-02 -5.79314444e-01 -1.22699469e+00
1.85888724e-02 -7.75128185e-01 9.63325198e-02 6.47902346e-01
1.82645132e-01 -1.05705264e+00 3.45563995e-01 4.92753807e-02
8.25314726e-01 -2.20499191e-02 1.20329302e-01 1.22806832e-01
-1.12030684e+00 2.22261631e+00 1.87600418e-03 6.92072895e-01
2.45869979e-01 -8.82225085e-01 6.04313646e-02 2.29747233e-02
-1.16056580e+00 -4.03568370e-02 1.35156411e-01 -1.14582165e+00
-1.07246765e+00 1.33256722e-01 -1.02487259e+00 8.10464202e-02
6.86890690e-01 -1.17454905e+00 -1.20774497e+00 -1.00404115e+00
1.42160222e-01 -1.06549172e+00 9.36139413e-02 1.55187808e+00
3.83658646e-01 1.45603194e+00 -3.17762926e-02 9.60053082e-01
4.10763374e-01 1.62567903e+00 1.33149595e-02 7.46648567e-02
1.97523365e-02 -1.16860384e+00 -2.87958594e-03 -1.57362237e+00
-1.28746538e+00 -1.25899561e+00 8.37318134e-02 7.42149496e-02
-1.08713055e+00 -1.06894127e+00 4.44763270e-02 -1.03432565e+00
-1.09227633e+00 -1.59380858e+00 8.65721377e-02 6.76256426e-02
-1.13848875e+00 -1.07253778e+00 -3.87649917e-02 -1.16112620e+00
-1.20768521e+00 2.25370733e-01 2.38637148e-02 -1.23131596e+00
-8.44707691e-01 1.08240810e-01 -1.09456305e+00 1.44985373e+00
-1.11918178e+00 1.11526433e-01 -1.00346216e+00 -1.39199724e-02
2.52400989e-01 -1.26478013e+00 -1.38160548e+00 1.81728412e+00
1.88392388e+00 1.88381131e+00 1.59729928e+00 1.94781842e-01
1.73517223e+00 -9.49785706e-01 1.16873184e-01 2.22775930e-02
1.46310430e-01 -1.19800318e+00 3.95371753e-02 1.92519571e-01
1.36067526e-01 1.53213157e+00 1.49169903e-01 -1.05339681e+00
-9.51683895e-01 1.62296546e+00 -1.27418211e+00 1.84554033e+00
-4.61666836e-03 -1.08242299e+00 1.30377768e-01 -2.76375453e-02
1.88864631e+00 -3.05505081e-02 -3.04672992e-02 1.62418274e+00
-9.92039197e-01 -1.63836166e+00 -5.39609903e-02 -5.38726024e-02
3.63765514e-01 1.76806048e+00 -9.70983331e-01 1.74342499e-01
-2.72399591e-02 1.87823608e+00 9.99567544e-01 9.85112670e-01
4.76677531e-01 8.97977344e-01 1.85156448e+00 -1.15136581e+00
3.64919032e-01 1.23049262e-01 2.55844877e-01 4.42297840e-01
1.72802148e+00 3.26785471e-01 2.17110369e-01 1.49287944e-01
1.04304163e+00 1.62667447e+00 4.83899311e-01 -1.20043843e+00
-1.26914311e+00 -1.16643399e+00 1.81136761e-01 1.19741262e-01
3.96712474e-01 3.96925582e-01 -9.92687446e-01 1.53226580e+00
1.08063662e-01 3.60935083e-01 1.01729135e-01 1.63014742e+00
1.61457837e+00 1.07572009e+00 -9.14439432e-01 7.36025553e-02
-1.06388358e+00 5.97396140e-01 1.81657736e+00 -9.47287513e-01
2.56188083e-01 7.87773107e-02 1.81690787e+00 -1.23518824e+00
1.07589613e-01 1.10526758e-01 1.23040484e-01 1.05228621e+00
1.32897301e-01 1.74453755e-01 -1.00361753e+00 1.60835808e+00
5.78074154e-02 -1.02195112e+00 5.31839293e-01 5.31617526e-01
4.96688930e-01 1.31593102e-01 -1.08978245e+00 1.81247111e+00
1.88227218e+00 -2.30785306e-02 2.95819776e-01 1.89010335e+00
2.32454885e-01 -9.74354978e-01 2.61436872e-01 -1.13515428e+00
-1.48767936e+00 1.94704938e-02 -1.27222723e+00 -1.00174779e+00
2.71576910e-02 -1.06647307e+00 1.23101689e-01 -1.03888672e+00
-9.26190481e-01 -1.10752599e+00 -8.71415243e-01 -1.21905108e+00
1.61718096e+00 -1.18820163e+00 -1.01555911e+00 1.01525467e+00
-1.59736305e+00 1.83565622e-01 1.71898154e-01 -1.20176922e+00
-1.02018732e+00 3.54988208e-01 3.20121200e-02 -9.92008654e-01
-1.55002630e+00 1.62639966e+00 -3.15208627e-02 -1.02794471e+00
-9.29802486e-01 -3.53809942e-02 1.73991277e+00 -1.11885378e+00
-1.03742829e+00 1.82671889e+00 1.77719492e+00 1.73046429e+00
-1.05707094e+00 -9.56541949e-01 3.25727775e-02 4.52383143e-01
-1.26440033e+00 4.25617467e-01 3.20342966e-02 -1.05456274e+00
1.37627860e-01 -1.00744599e+00 -1.00014629e+00 1.51139979e+00
1.81048887e+00 9.75963248e-01 2.02047271e-01 -1.00780055e+00
1.16923022e-01 1.59742271e+00 8.10912649e-02 1.87827940e+00
1.87699925e+00 -2.09184452e-02 -4.13692077e-02 7.39050076e-02
-2.37719294e-02 -1.62077911e+00 1.81734535e+00 -1.05569150e+00
1.72920027e+00 1.23676011e-01 1.30409758e-01 -1.60690428e+00
2.25706700e-02 -9.51904772e-01 4.12873595e-01 -1.06882352e+00
-1.07472490e+00 5.73738262e-01 -1.19034095e+00 -1.11490315e+00
-1.89609773e-02 -1.25963036e+00 1.73041564e+00 1.35776123e-01
-4.84722560e-02 1.50182753e+00 -9.57393267e-01 1.68979893e+00
4.70339059e-01 -1.15491013e+00]
Unique values in p90_1200: [-0.46440209 -0.7740401 -1.00290733 -0.8891155 0.05141904 -1.24058306
0.69883952 -0.76724366 0.50880788 -0.57167349 0.18291459 0.57258939
-1.27805142 0.06479819 0.85372265 0.84543013 -0.6490723 -1.6064613
0.37333094 -0.86672327 0.86078777 -1.31138878 0.50851477 0.29350177
-0.65205834 0.06984209 0.64829062 -0.67736943 -0.62796853 0.6350397
-0.71110738 0.79154102 -1.05950158 -0.77720322 -0.78395081 -0.64705108
0.90857047 -0.5701591 0.74174931 2.67399014 0.11269081 1.16547958
-0.27110351 0.9816032 0.1486576 1.55046465 0.3781489 0.62774864
0.47835518 -0.67160498 -0.48251372 -1.42507741 -1.15260915 -0.9839237
0.80234327 0.75669781 -1.15014888 -0.61459548 0.52570433 -0.65929444
-0.53586547 -1.34364287 0.59903627 0.54558682 -0.56628763 -0.62793189
-0.63342767 -0.67242934 -0.8845479 0.91855446 0.3178908 -1.07634125
-1.27633429 0.6296172 -0.5388454 1.82399542 -0.90241527 0.81421414
-0.72904803 0.22968973 -0.7390137 -1.05742418 -1.35069394 -0.37392333
-0.711889 -0.71471627 2.08183271 0.18324434 0.98315423 -0.97212611
0.8582475 0.45727584 0.91785222 -0.94753557 0.40770395 0.74049749
0.76753059 2.15249615 0.94064136 -0.60117969 -0.84536914 2.92471955
-1.0439864 -0.51709435 -0.12620437 -0.8587605 0.87094884 -0.1821941
-0.79025874 0.06911543 0.0693719 2.35121723 -0.6836224 -1.32415607
-0.4859272 -0.48562799 -0.01430431 0.16600592 -0.76295085 1.02837223
-0.53078493 -0.69213475 0.90401508 2.41533459 -0.61427184 0.60393362
-0.39919168 -0.69796637 0.36771915 0.84754295 -1.53399388 -1.44546368
1.46750289 0.24056526 0.85630566 0.73031199 -0.24171944 1.51197591
-0.22698465 -0.97850121 -0.99092165 -0.69648862 0.31593674 0.84623007
-0.71653598 -0.71450865 -0.68703589 2.15483491 0.62829211 -0.73497125
0.88767432 1.40020408 0.86992297 0.56721574 -0.9887783 0.73751756
-0.5535985 -0.54273519 -0.37507134 -0.85516383 0.87461269 0.61212843
-0.37357526 -1.08455193 0.88601948 0.79191351 0.847488 3.39251372
0.8855676 1.02840887 -0.72869996 2.4291473 0.60823864 -0.6007156
-0.42425241 -0.4234891 -0.4473713 0.88510962 -0.57680898 -0.37157236
-0.74491249 0.06275255 -0.84673697 -0.73718788 -0.03588438 -0.73810384
0.8074238 -0.56798521 -1.28354291 0.36280959 -1.13872683 -0.66234154
0.39103955 -0.56876683 0.8495825 -0.64845556 -0.93073072 -0.5016329
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3.16020736 -1.31297401 0.78458581 0.26632822 -1.0344262 -0.77791156
0.39768333 0.46518364 -0.68366515 -1.32773504 2.80546126 -0.05791022
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0.6839643 -0.63543668 -0.68036158 1.90550386 -0.36249212 1.86577552
0.99139789 -0.64626335 0.72317359 3.44041245 0.63914931 -0.69222634
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0.01604457 -0.87118706 -0.7420669 -0.61297728 -0.65685188 -0.45919332
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Unique values in p00_1200: [-5.08015517e-01 -7.31805479e-01 -9.16579967e-01 -8.13687716e-01
2.97992988e-02 -1.16297618e+00 5.78638089e-01 -7.94808204e-01
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6.93772524e-01 -1.22738722e+00 4.10202902e-01 2.15109143e-01
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3.03728951e-02 1.29619613e+00 -2.99816449e-01 1.44487229e+00
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4.99978917e-01 -5.56541764e-01 -5.05715669e-01 -5.05081982e-01
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3.05027192e-01 -5.33057093e-01 7.12198622e-01 -6.15474687e-01
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1.38337731e+00 -5.33363011e-01 -7.98593940e-01 2.30361342e-01
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8.14511813e-01 -6.06908982e-01 5.97725189e-01 3.45685145e+00
5.25555849e-01 -6.98247364e-01 -6.78133254e-01 -3.36024032e-01
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7.30559166e-01 -1.22421987e+00 -2.22376312e-03 -8.36391204e-01
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1.38488505e+00 6.49332468e-01 -2.74556360e-01 1.89964129e+00
-8.20254028e-01 2.34002122e+00 -3.73934105e-02 -5.77300487e-01]
Unique values in p10_1200: [-0.55484798 -0.67951526 -0.85797515 -0.76463416 0.05124564 -1.11664732
0.51146131 -0.84850242 0.30479419 -0.48019628 0.01334469 0.42912495
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0.30649687 -0.78248467 0.56645936 -1.17270967 0.3569444 0.17895664
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0.70820366 -0.46522375 0.54923166 2.66640055 -0.04251726 1.32803925
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0.37236395 -0.56660099 -0.41019557 -1.29437692 -1.03291868 -0.8717774
0.64900165 0.62412947 -1.02310994 -0.50709761 0.38718078 -0.57537556
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0.64559126 0.3902245 0.67687737 -0.81408218 0.27513543 0.49449479
0.54882985 2.31092227 0.69287452 -0.49495284 -0.7611836 2.88741739
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1.37373535 0.06308905 0.59178358 0.51151153 0.02335986 1.66704826
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0.26944978 -0.47983465 0.63061371 -0.56345681 -0.84591074 -0.41065263
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4.16956013 -1.17081362 0.53892518 0.10300408 -0.89642356 -0.69812419
0.10125619 0.33710995 -0.59000655 -1.19096852 2.84675399 -0.04930286
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0.47644337 -0.53548565 -0.59035312 2.05107028 -0.39501208 2.62165876
0.69035817 -0.55474753 0.51987928 3.39411772 0.46414791 -0.72030925
-0.6942467 -0.35322361 -0.22541717 0.61943329 -0.37497672 -1.3059702
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0.02109465 -0.787658 -0.82852231 -0.50558077 -0.54921758 -0.56120164
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-0.28938569 1.91377103 -0.77126406 2.27052005 -0.13138306 -0.52473717]
Unique values in p90u_1200: [-5.93106582e-01 -9.64721121e-01 -1.18332857e+00 -9.08713196e-01
1.76309166e-01 -1.47426642e+00 9.96001468e-01 -9.08642492e-01
7.05455799e-01 -3.89150151e-01 -2.90171376e-02 9.20271789e-01
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9.12936339e-01 -1.60116086e+00 8.24358576e-01 5.91413862e-01
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1.23967317e+00 -9.47910347e-01 1.15801106e+00 7.59245414e-01
1.18264668e+00 -9.80113413e-01 7.15641469e-01 9.59081178e-01
1.01253937e+00 1.54743063e+00 1.19273292e+00 -4.04947881e-01
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2.11892725e-01 1.14200343e+00 -1.84321324e+00 -1.73223893e+00
1.77358122e+00 5.21147023e-02 9.49160645e-01 9.65223512e-01
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7.99877618e-01 -1.43364526e+00 9.26933349e-01 -4.61963326e-01
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1.14959297e+00 -4.82411996e-01 9.85362856e-01 3.32128375e+00
9.46155762e-01 -6.47779986e-01 -7.66815331e-01 -1.59585931e-01
1.91383213e-02 1.08791553e+00 -5.14327831e-01 -1.64718970e+00
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1.16426387e+00 -1.44591667e+00 1.22972494e-01 -8.13567775e-01
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1.70146403e+00 1.02723237e+00 -6.81137582e-02 8.39405086e-01
-7.89760706e-01 2.53800356e+00 -1.41744438e-01 -4.79517587e-01]
Unique values in p00u_1200: [-6.60152234e-01 -9.53321916e-01 -1.12000468e+00 -8.41270259e-01
1.48003467e-01 -1.43765186e+00 8.91488928e-01 -9.72020972e-01
5.95329216e-01 -3.59929308e-01 -1.04319025e-01 8.26456825e-01
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7.77201986e-01 -1.56488526e+00 7.31028159e-01 5.09950026e-01
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1.03529362e+00 -3.14861750e-01 9.50214261e-01 2.59268325e+00
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1.00494875e+00 -4.56464243e-01 8.78523863e-01 3.52063656e+00
8.44250187e-01 -6.92337438e-01 -7.91653556e-01 -2.08463440e-01
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1.04761767e+00 -1.36717940e+00 9.70426979e-02 -8.10935729e-01
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1.74232388e+00 9.15940585e-01 -1.16074430e-01 1.05189800e+00
-7.86266954e-01 2.63471115e+00 -2.34248824e-01 -4.52401029e-01]
Unique values in p10u_1200: [-7.34917416e-01 -9.19337092e-01 -1.07985274e+00 -8.01030316e-01
1.79466807e-01 -1.42813120e+00 8.43131026e-01 -1.07547678e+00
5.16117799e-01 -2.94006913e-01 -1.76696467e-01 8.04402400e-01
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6.68871941e-01 -1.55169978e+00 6.89321366e-01 4.80072359e-01
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1.24695044e+00 -9.18488732e-01 9.93764267e-01 7.19270450e-01
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1.79842396e+00 -9.93181336e-02 7.41554700e-01 7.83666901e-01
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9.88427491e-01 -1.35509430e+00 1.29541802e-01 -7.69167489e-01
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-7.43154401e-01 2.64459411e+00 -3.40844283e-01 -3.91292115e-01]
Unique values in p90r_1200: [-2.52291243e-01 -4.44835359e-01 -6.44419989e-01 -7.14628835e-01
-8.50404057e-02 -7.86412518e-01 2.73973898e-01 -4.89553507e-01
2.19599157e-01 -6.58732279e-01 3.67550345e-01 1.18071814e-01
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6.57981405e-01 -7.87642155e-01 9.76406742e-02 -6.17089038e-02
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5.20171895e-01 -6.78254164e-01 2.88549036e-01 2.47490999e+00
3.67268319e-01 2.00860710e+00 -3.93397080e-01 2.30753281e+00
2.83077719e-01 1.41900216e+00 -2.17931059e-02 1.94746967e-01
3.90140992e-02 -6.30168618e-01 -2.89550350e-01 -9.63385379e-01
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5.50393873e-01 -8.27952790e-01 4.03029300e-01 6.94650823e-02
4.87975731e-01 -7.49643010e-01 2.24162660e-02 3.88634659e-01
3.83998140e-01 2.39638249e+00 5.19777058e-01 -6.97110467e-01
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4.62954326e-01 3.99599856e-01 -9.51713198e-01 -9.01483572e-01
8.99982845e-01 3.91195461e-01 6.12710485e-01 3.63545567e-01
7.16236833e-01 1.37884156e+00 -2.49529327e-02 -7.38699247e-01
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2.60007938e-01 -4.41390122e-01 3.80489729e-01 1.25963453e+00
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3.93429112e-01 3.44119570e-01 3.99532169e-01 5.77907799e+00
4.44994868e-01 6.11300351e-01 -7.30209117e-01 2.15532865e+00
1.47727482e-01 -6.83849573e-01 -1.69672095e-01 -1.68490968e-01
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2.78526120e-02 -7.39508100e-01 6.46689057e-01 -6.36280700e-01
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8.79507709e-01 -6.58710845e-01 -7.61537767e-01 -3.71613029e-02
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4.27481323e-02 -7.81460130e-01 3.17022448e-01 -7.02173410e-01
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6.57597849e-01 -7.01301383e-01 3.29792615e-01 2.96431650e+00
2.14601645e-01 -6.17344302e-01 -4.84580813e-01 -4.06814781e-01
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4.19759122e-01 -9.66507188e-01 -9.59379161e-02 -7.78661298e-01
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3.97467734e-01 -3.83792380e-01 2.45494250e+00 -7.72924875e-01
1.76270241e+00 2.57537384e-01 -6.48693257e-01]
Unique values in p00r_1200: [-2.82517974e-01 -4.04765227e-01 -5.76372446e-01 -6.55143259e-01
-8.58730487e-02 -7.15787498e-01 1.90970674e-01 -4.99038342e-01
1.35828972e-01 -6.09035762e-01 2.57183371e-01 5.88264161e-02
-7.32310620e-01 -2.32547261e-01 3.06033413e-01 2.94429225e-01
-6.44386362e-01 -9.23068975e-01 -2.76175081e-02 -3.76040744e-01
5.02461184e-01 -7.10791009e-01 4.25185099e-02 -9.64011584e-02
-5.83674263e-01 -2.36708094e-01 2.05386235e-01 -6.07021267e-01
-6.32432207e-01 1.98096052e-01 -6.66768774e-01 2.41391207e-01
-4.98634086e-01 -6.11536915e-01 -6.20444122e-01 -6.47455607e-01
4.16084151e-01 -6.25207946e-01 2.03011109e-01 2.37905312e+00
2.58870196e-01 1.99491284e+00 -3.88007501e-01 2.72981561e+00
1.57825547e-01 1.44683069e+00 -6.22022517e-02 1.23652816e-01
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Unique values in p10r_1200: [-0.30872637 -0.361318 -0.52663565 -0.60973226 -0.06855705 -0.66565705
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Unique values in p00r_30: [-1.65937436e-01 -5.12415908e-01 -4.63430960e-01 -5.57547809e-01
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Unique values in p90u_150: [-0.79090501 -1.00391685 0.4674096 -0.91817178 -0.33014982 0.63867743
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Unique values in p00u_150: [-0.7736289 -0.99117993 0.47079923 -0.89372018 -0.35619204 0.65221175
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Unique values in p10_600: [-1.06882346e+00 -7.21464537e-01 -3.27728687e-01 -9.34511551e-01
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Unique values in p90u_600: [-1.16208916 -0.90181161 -0.41087373 -1.05698568 -0.71049175 -1.06853125
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Unique values in p00u_600: [-1.19885574 -0.93195859 -0.32698204 -1.03834114 -0.7138696 -1.06051803
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0.88332028 0.28918629 0.36983796 -0.11803938 1.41746619 -0.59593653
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0.71752219 -0.05129253 0.07772543 -0.75549848 -0.48817133]
Unique values in p10u_600: [-1.23376266e+00 -9.07064174e-01 -2.45380816e-01 -1.02670392e+00
-6.72779408e-01 -1.05793120e+00 1.66066940e+00 -1.23357725e+00
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1.06332465e-01 -1.03007112e+00 1.45991928e+00 -2.67472485e-01
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2.20148851e-01 -5.77174742e-01 2.99718688e-01 6.10636433e-01
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1.64938435e+00 7.10031920e-02 -5.74118854e-01 -5.73301661e-01
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2.42007036e-01 4.71901395e-01 -4.72196250e-02 1.40077010e+00
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4.12360789e-01 8.53548647e-01 5.84419888e-01 2.35966220e-01
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4.53227286e-01 -1.22784317e+00 2.83971598e-03 6.18261275e-01
1.57533343e+00 -8.96754271e-01 -6.60752714e-01 -1.48592563e-02
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2.41393570e-01 1.08893216e+00 -2.84178007e-01 7.68739164e-01
2.40478094e-02 5.96014097e-02 -8.70089071e-01 -4.40802009e-01]
Unique values in p90r_600: [-6.52492040e-01 -3.68825967e-01 -4.49829449e-01 -6.88846984e-01
-4.31625383e-01 -8.25659720e-01 6.41311739e-01 -5.83994900e-01
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3.31211477e-01 -5.82250374e-01 7.63201680e-01 2.79089133e+00
2.90247011e-01 1.46534177e+00 -6.02535815e-01 2.91410740e+00
2.96235843e-01 2.65352047e-01 -4.45709264e-02 6.46694243e-01
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2.64139388e-01 -4.86400686e-01 8.77280254e-01 -5.54674796e-01
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6.41620223e-01 -4.12648336e-01 -7.41715363e-01 -6.23667282e-01
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8.91747061e-01 -9.30918222e-01 -4.08070727e-01 -6.73361473e-01
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-6.57735194e-01 1.65389247e-01 -4.74323757e-02 -6.17544064e-01]
Unique values in p00r_600: [-0.62200101 -0.36056015 -0.38658554 -0.62824953 -0.40878055 -0.75951772
0.52573835 -0.55335697 -0.12707955 -0.40389644 0.31418025 0.324763
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-0.33580348 -0.62020755 0.89471752 -0.63057228 0.37992956 0.01721223
-0.5476553 -0.35391376 0.22180904 -0.52364351 -0.60603181 0.20710522
-0.66457153 0.27522846 -0.49006637 -0.57857995 -0.60850445 -0.52036422
0.25043242 -0.51561031 0.63662559 2.67245299 0.18504042 1.58574536
-0.56845745 3.47534913 0.13790403 0.32610439 -0.07493188 0.54049062
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0.27825946 0.02236886 -0.78714218 -0.54131171 0.31915217 -0.5242158
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0.60464116 -0.31194309 0.83734357 -0.66781963 0.25380861 0.23942879
0.54136873 1.59390876 0.85565672 -0.57418031 -0.62732903 2.97143461
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0.48104857 0.14061709 -0.24084639 3.1412128 -0.71726666 -0.08588101
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0.78384845 0.95868941 -0.55374758 1.52419584 0.47465047 -0.48944564
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0.7591372 -0.55443795 -0.83673243 0.11663558 -0.74671547 -0.50661724
0.20429224 -0.46020151 0.73078633 -0.52430361 -0.67869549 -0.44408061
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5.94099365 -0.85344076 0.51967636 -0.45736128 -0.71721094 -0.7028072
0.46419792 0.20269953 -0.53726331 -0.84701511 1.4377867 -0.36313393
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-0.30460329 -0.2148967 -0.02173047 -0.40385708 -0.61915594 0.05092563
-0.24507646 -0.75996011 0.08262846 0.17635923 0.20294177 -0.74725475
0.27541014 -0.55733269 -0.52340127 1.54633029 -0.25645859 2.67430005
1.04833242 -0.49015721 0.52250145 4.01114817 0.53394112 -0.40627642
-0.69776836 -0.58693562 -0.43679835 -0.04511245 -0.34792141 -0.84345483
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-0.74533411 -0.46377452 -0.20736311 -0.69868341 -0.02271759 0.57265975
-0.50954529 1.55329159 -0.61574735 0.14402355 -0.14418152 -0.57821659]
Unique values in p10r_600: [-5.99107008e-01 -3.17109910e-01 -3.39928183e-01 -5.84963231e-01
-3.67126617e-01 -7.14855461e-01 4.47150213e-01 -5.54806305e-01
-1.59155051e-01 -3.65305137e-01 2.14673421e-01 2.83938085e-01
-6.48341313e-01 -2.27819730e-01 5.63151844e-01 6.38739222e-01
-6.36007446e-01 -8.38389955e-01 -3.12710915e-01 -5.92269329e-01
7.31567432e-01 -5.71842388e-01 3.21427745e-01 8.55362395e-04
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-5.63321790e-01 1.44674246e-01 -6.20292676e-01 2.38471042e-01
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2.05418043e-01 -4.74394830e-01 5.55370869e-01 2.54003864e+00
9.60619889e-02 1.57962908e+00 -5.34697382e-01 3.95272137e+00
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Unique values in p90u_300: [-1.19151445e+00 -7.16400905e-01 6.53952122e-01 -1.16935750e+00
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6.20325755e-01 8.67853529e-01 -5.37093801e-01 -3.60175113e-01]
Unique values in p00u_300: [-1.19804562 -0.73602362 0.77950701 -1.16242274 -0.7148587 -0.38126612
2.01089484 -1.03237611 -0.86252444 -0.42272146 -0.66838098 -0.05469897
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0.03416766 0.92235539 1.94040422 2.62578222 2.30324479 1.27477305
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0.44685982 1.74669978 0.5693646 0.94167089 -0.64364722 -0.33008509]
Unique values in p10u_300: [-1.21575145 -0.70899805 0.93526138 -1.16641827 -0.68279415 -0.34959293
1.93721006 -1.07953771 -0.89169235 -0.38227541 -0.74830107 -0.04821113
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0.13059431 0.54070385 -1.00790481 0.22711438 -0.2031009 2.19530857
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0.46278485 1.82805079 0.67189938 0.93192273 -0.74435487 -0.28257566]
Unique values in p90r_300: [-7.38980930e-01 -1.54835424e-01 -1.56248425e-01 -7.12228548e-01
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6.72268631e-01 1.25711900e-01 -6.52640214e-01 -6.71223328e-01
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1.41787894e-01 -8.89173098e-01 -1.47933800e-01 2.40029825e-01
1.30000648e+00 -8.45228744e-01 -5.96459310e-01 -5.43861617e-01
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-5.09842530e-01 -1.65667310e-01 -1.53132835e-01 -5.74149884e-01]
Unique values in p00r_300: [-6.75829057e-01 -1.93095414e-01 -1.11893739e-01 -6.38663700e-01
-5.17095478e-01 -6.09831392e-01 7.82043991e-01 -6.09721524e-01
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-6.18528788e-01 -1.32762043e-01 7.31814699e-01 1.05507949e+00
-6.94063351e-01 -8.25629644e-01 -3.31641494e-01 -8.11637081e-01
7.78156362e-01 -7.10155694e-01 2.03857790e-01 8.28154313e-02
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1.72362362e-01 -3.85474825e-01 8.73957305e-01 2.73219697e+00
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1.10134603e+00 -6.37421349e-01 1.49562451e-01 -2.03907868e-02
7.68399728e-01 1.65561487e+00 9.28421671e-01 -4.72748338e-01
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1.79598268e+00 -2.37934613e-01 -1.69516100e-01 -5.85930925e-01
1.79226750e-01 -5.63870040e-01 -4.01890197e-01 7.29213556e-01
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6.98425827e-02 -4.40891379e-01 9.64753160e-01 -6.08365549e-01
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7.19760834e-02 -8.02161127e-01 -1.62232899e-01 1.70503061e-01
1.09564606e+00 -7.59508391e-01 -5.48004945e-01 -5.07043047e-01
-6.92412518e-01 -4.32307607e-01 -5.31069345e-01 -5.00271624e-01
-7.15181440e-01 -3.99661853e-01 -1.13450669e-01 -6.64323929e-01
-9.18470964e-02 9.20252016e-01 -1.89484803e-01 2.33581846e+00
-4.77950624e-01 -2.01113884e-01 -2.29644760e-01 -5.30945391e-01]
Unique values in p10r_300: [-6.36833335e-01 -1.56917334e-01 -6.84671853e-02 -5.93155396e-01
-4.73249968e-01 -5.67343067e-01 6.73190659e-01 -5.92606603e-01
-3.24661412e-01 -4.18584567e-01 4.71150478e-02 -2.06658388e-02
-6.01324285e-01 -1.31786436e-01 6.39102501e-01 9.04686722e-01
-6.47267031e-01 -7.75412226e-01 -3.11288939e-01 -7.62299808e-01
6.41838175e-01 -6.57627775e-01 1.72728086e-01 6.82336233e-02
-5.04137388e-01 -4.68170733e-01 2.95918114e-02 -4.53671660e-01
-5.90203355e-01 1.47055990e-02 -5.91871287e-01 7.00094579e-03
-4.42782019e-01 -5.40593147e-01 -5.78888441e-01 -4.50503252e-01
1.51389828e-01 -3.46589084e-01 7.63807793e-01 2.57956592e+00
-2.22770814e-01 4.32907278e-01 -4.31088256e-01 5.61086697e+00
-1.35819483e-01 -1.49716708e-01 -1.05955040e-01 4.30155024e-01
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-6.81986052e-01 -3.94198933e-01 -2.57699436e-02 -5.60168139e-01
-3.74774818e-01 -7.41389475e-01 2.86183954e-01 2.63627078e-01
-4.38077489e-01 -5.90156931e-01 -7.35893506e-01 -5.04447431e-01
-5.66886292e-01 6.06854705e-01 9.98348048e-02 -6.15765328e-01
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9.31048673e-01 -5.91880406e-01 1.30159338e-01 -6.43785959e-02
6.37975071e-01 1.56918197e+00 7.94049427e-01 -4.31438090e-01
-4.86125046e-01 7.51853725e-01 -6.56501174e-01 1.81434295e-02
-3.71201032e-01 -5.72708304e-01 9.32714947e-01 -2.23993577e-01
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-4.32900433e-01 -7.46182293e-01 -5.56157475e-01 -5.56246177e-01
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1.95003106e-01 7.94687751e-01 -7.38245439e-01 -7.53338071e-01
-5.86361672e-02 2.24631286e-01 9.99011242e-02 1.57797937e-01
1.89947732e+00 -2.17129522e-01 -2.29741809e-01 -5.41778606e-01
2.10438940e-01 -5.20408017e-01 -3.81786433e-01 6.36184448e-01
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3.40864278e-01 -6.17104151e-01 3.48631935e-01 -3.74547674e-01
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6.64635459e-01 3.71578438e-01 1.90561499e-02 -6.18290438e-01
4.88516073e-01 9.04239066e-01 7.95458714e-01 9.19184787e+00
8.43764087e-01 5.38993406e-01 -4.61223779e-01 1.08442051e+00
9.63779075e-02 -4.00356687e-01 -3.16459364e-01 -3.15063341e-01
-4.53731348e-01 8.70581984e-01 -3.53715101e-01 -1.23145717e-02
-4.77397416e-01 -1.36931162e-01 -6.29582141e-01 -1.72270269e-01
-5.94160134e-01 -4.62429134e-01 4.20149089e-01 -4.41424130e-01
-7.32757345e-01 6.54979492e-02 -6.57573061e-01 -3.86648473e-01
5.89489111e-02 -4.00698232e-01 8.26728298e-01 -5.64129893e-01
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-5.87488405e-01 2.52204215e+00 -5.63873734e-01 -4.85238024e-01
3.86382401e+00 -7.08130226e-01 2.21016880e-01 -4.69117442e-01
-6.32709928e-01 -6.76132538e-01 9.34930147e-02 -3.05938491e-02
-4.33266847e-01 -7.26653062e-01 1.20600880e+00 -4.70164460e-01
-3.13465044e-01 -5.41728867e-01 -3.37630166e-01 -3.91954022e-01
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-4.12812294e-01 -6.82678260e-01 -1.31973788e-01 -9.73874061e-02
-3.05192398e-02 -7.30282058e-01 8.75242711e-02 -5.06098783e-01
-3.31725254e-01 1.78969389e+00 -1.90567650e-02 2.38113006e+00
4.79936004e-01 -3.43806157e-01 6.90682393e-01 3.41504910e+00
4.81593988e-01 1.53057893e-02 -5.82623879e-01 -5.80949315e-01
-1.17696056e-01 -2.44404193e-01 -3.41583629e-01 -7.04612481e-01
1.86391669e-02 -7.53138284e-01 -1.63750716e-01 1.13728714e-01
9.33461040e-01 -7.10850398e-01 -5.02447073e-01 -4.65452468e-01
-6.61077211e-01 -3.92122308e-01 -4.88256385e-01 -5.02754629e-01
-6.67470399e-01 -3.60421647e-01 -1.13482289e-01 -6.18088993e-01
-1.06223634e-01 7.72437601e-01 -1.71756294e-01 2.21078046e+00
-4.37206218e-01 -1.99622036e-01 -2.87220810e-01 -4.88369957e-01]
Unique values in Country_encoded: [ 0.60568138 0.51463124 -1.67057217 1.06093209 -1.9437226 -1.03322118
0.15043067 -1.57952203 -1.12427132 0.96988195 0.69673152 -0.48692033
0.24148081 -0.12271976 -1.48847189 -0.76007075 -0.66902061 -1.39742175
0.05938053 -0.03166961 0.87883181 0.78778166 -1.76162231 -1.3063716
-0.57797047 -0.30482004 -0.85112089 0.4235811 -0.39587018 0.33253095
-0.2137699 -1.21532146 -1.85267246 -0.94217104]
# Plot histograms for Latitude and Longitude
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.hist(df['Latitude'], bins=20)
plt.xlabel('Latitude')
plt.ylabel('Frequency')
plt.subplot(1, 2, 2)
plt.hist(df['Longitude'], bins=20)
plt.xlabel('Longitude')
plt.ylabel('Frequency')
plt.tight_layout()
plt.show()
# Calculate mean, median, and standard deviation for p90_1200, p00_1200, and p10_1200
statistics_1200 = df[['p90_1200', 'p00_1200', 'p10_1200']].describe()
statistics_300 = df[['p90_300', 'p00_300', 'p10_300']].describe()
# Create a DataFrame to compare statistics across time intervals
comparison_stats = pd.concat([statistics_1200, statistics_300], keys=['1200', '300'])
comparison_stats = comparison_stats.rename(index={'1200': 'Statistics for 1200', '300': 'Statistics for 300'})
print(comparison_stats)
p90_1200 p00_1200 p10_1200 \
Statistics for 1200 count 2.760000e+02 2.760000e+02 2.760000e+02
mean -5.792468e-17 -1.287215e-17 1.930823e-17
std 1.001817e+00 1.001817e+00 1.001817e+00
min -1.606461e+00 -1.536982e+00 -1.498838e+00
25% -7.151712e-01 -7.026149e-01 -6.833262e-01
50% -3.670322e-01 -3.666486e-01 -3.975284e-01
75% 7.049230e-01 5.991319e-01 5.246408e-01
max 3.440412e+00 3.963610e+00 4.471422e+00
Statistics for 300 count NaN NaN NaN
mean NaN NaN NaN
std NaN NaN NaN
min NaN NaN NaN
25% NaN NaN NaN
50% NaN NaN NaN
75% NaN NaN NaN
max NaN NaN NaN
p90_300 p00_300 p10_300
Statistics for 1200 count NaN NaN NaN
mean NaN NaN NaN
std NaN NaN NaN
min NaN NaN NaN
25% NaN NaN NaN
50% NaN NaN NaN
75% NaN NaN NaN
max NaN NaN NaN
Statistics for 300 count 2.760000e+02 2.760000e+02 2.760000e+02
mean 8.366898e-17 -1.287215e-17 -1.351576e-16
std 1.001817e+00 1.001817e+00 1.001817e+00
min -1.401155e+00 -1.351009e+00 -1.335402e+00
25% -7.848346e-01 -7.573392e-01 -7.504482e-01
50% -2.745483e-01 -2.833819e-01 -2.697757e-01
75% 6.557133e-01 5.757628e-01 5.839749e-01
max 3.903643e+00 4.980302e+00 5.563019e+00
# Scatter plot of Latitude and Longitude on a map
plt.figure(figsize=(10, 8))
plt.scatter(df['Longitude'], df['Latitude'])
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.title('Geographic Distribution')
plt.show()
# Select variables for correlation analysis
variables = ['p90_1200', 'p00_1200', 'p10_1200', 'Latitude', 'Longitude']
# Compute correlation matrix
corr_matrix = df[variables].corr()
# Plot correlation matrix as a heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix for Multiple Variables')
plt.show()
# Create a scatter plot of p90_1200 vs. Longitude with color encoding for Latitude
plt.scatter(df['Longitude'], df['p90_1200'], c=df['Latitude'], cmap='viridis')
plt.colorbar(label='Latitude')
plt.xlabel('Longitude')
plt.ylabel('p90_1200')
plt.title('Scatter Plot of p90_1200 vs. Longitude with Color Encoding for Latitude')
plt.show()
df = df.drop("Country", axis=1)
df
| Latitude | Longitude | p90_1200 | p00_1200 | p10_1200 | p90u_1200 | p00u_1200 | p10u_1200 | p90r_1200 | p00r_1200 | ... | p90_300 | p00_300 | p10_300 | p90u_300 | p00u_300 | p10u_300 | p90r_300 | p00r_300 | p10r_300 | Country_encoded | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.352765 | 0.251194 | -0.464402 | -0.508016 | -0.554848 | -0.593107 | -0.660152 | -0.734917 | -0.252291 | -0.282518 | ... | -1.172443 | -1.136296 | -1.125151 | -1.191514 | -1.198046 | -1.215751 | -0.738981 | -0.675829 | -0.636833 | 0.605681 |
| 1 | -0.126329 | -0.064588 | -0.774040 | -0.731805 | -0.679515 | -0.964721 | -0.953322 | -0.919337 | -0.444835 | -0.404765 | ... | -0.586885 | -0.600261 | -0.556108 | -0.716401 | -0.736024 | -0.708998 | -0.154835 | -0.193095 | -0.156917 | 0.514631 |
| 2 | -4.916056 | -0.579314 | -1.002907 | -0.916580 | -0.857975 | -1.183329 | -1.120005 | -1.079853 | -0.644420 | -0.576372 | ... | 0.414370 | 0.496365 | 0.605076 | 0.653952 | 0.779507 | 0.935261 | -0.156248 | -0.111894 | -0.068467 | -1.670572 |
| 3 | -0.469286 | -1.226995 | -0.889115 | -0.813688 | -0.764634 | -0.908713 | -0.841270 | -0.801030 | -0.714629 | -0.655143 | ... | -1.145335 | -1.094991 | -1.071174 | -1.169357 | -1.162423 | -1.166418 | -0.712229 | -0.638664 | -0.593155 | 1.060932 |
| 4 | -0.020197 | 0.018589 | 0.051419 | 0.029799 | 0.051246 | 0.176309 | 0.148003 | 0.179467 | -0.085040 | -0.085873 | ... | -0.741186 | -0.728155 | -0.685837 | -0.701982 | -0.714859 | -0.682794 | -0.559051 | -0.517095 | -0.473250 | 0.514631 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 271 | -1.506439 | 1.501828 | 1.792834 | 1.899641 | 1.913771 | 0.839405 | 1.051898 | 1.110167 | 2.454943 | 2.384831 | ... | 1.727114 | 2.251499 | 2.276462 | 1.196198 | 1.746700 | 1.828051 | 2.090747 | 2.335818 | 2.210780 | -1.397422 |
| 272 | 0.096301 | -0.957393 | -0.854920 | -0.820254 | -0.771264 | -0.789761 | -0.786267 | -0.743154 | -0.772925 | -0.718363 | ... | 0.245594 | 0.188036 | 0.253678 | 0.620326 | 0.569365 | 0.671899 | -0.509843 | -0.477951 | -0.437206 | 1.060932 |
| 273 | -0.461599 | 1.689799 | 2.357024 | 2.340021 | 2.270520 | 2.538004 | 2.634711 | 2.644594 | 1.762702 | 1.682191 | ... | 0.566912 | 0.570582 | 0.541617 | 0.867854 | 0.941671 | 0.931923 | -0.165667 | -0.201114 | -0.199622 | -0.395870 |
| 274 | 0.461080 | 0.470339 | 0.061055 | -0.037393 | -0.131383 | -0.141744 | -0.234249 | -0.340844 | 0.257537 | 0.153308 | ... | -0.455104 | -0.551699 | -0.640980 | -0.537094 | -0.643647 | -0.744355 | -0.153133 | -0.229645 | -0.287221 | 0.878832 |
| 275 | 0.074844 | -1.154910 | -0.616189 | -0.577300 | -0.524737 | -0.479518 | -0.452401 | -0.391292 | -0.648693 | -0.600271 | ... | -0.497452 | -0.464913 | -0.420298 | -0.360175 | -0.330085 | -0.282576 | -0.574150 | -0.530945 | -0.488370 | 1.060932 |
276 rows × 57 columns
# Get all the features (column names)
features = df.columns.tolist()
# Print the list of features
print("All Features:", features)
All Features: ['Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300', 'Country_encoded']
from scipy.stats import chi2_contingency
from itertools import combinations
# Perform chi-square tests for all possible combinations of features
def perform_chi_square_tests(data, features):
results = []
for feature1, feature2 in combinations(features, 2):
contingency_table = pd.crosstab(data[feature1], data[feature2])
chi2, p_value, _, _ = chi2_contingency(contingency_table)
results.append((feature1, feature2, chi2, p_value))
return results
# Specify the list of features for the chi-square tests
features =['Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300', 'Country_encoded']
# Perform the chi-square tests
chi_square_results = perform_chi_square_tests(df, features)
# Print the results
for result in chi_square_results:
feature1, feature2, chi2_statistic, p_value = result
print("Chi-square test between", feature1, "and", feature2)
print("Chi-square statistic:", chi2_statistic)
print("P-value:", p_value)
print()
Chi-square test between Latitude and Longitude Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between Latitude and p90_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90r_1200 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between Latitude and p00r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between Latitude and p00u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between Latitude and p10u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between Latitude and p90r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00_75 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between Latitude and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between Latitude and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between Latitude and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between Latitude and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between Latitude and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between Latitude and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between Latitude and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between Longitude and p90_1200 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00_1200 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p10_1200 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90u_1200 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00u_1200 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p10u_1200 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90r_1200 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between Longitude and p00r_1200 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p10r_1200 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90_30 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00_30 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p10_30 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90u_30 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between Longitude and p00u_30 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between Longitude and p10u_30 Chi-square statistic: 75072.0 P-value: 0.2422935108134661 Chi-square test between Longitude and p90r_30 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00r_30 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p10r_30 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00_75 Chi-square statistic: 75072.0 P-value: 0.2422935108134661 Chi-square test between Longitude and p10_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90u_75 Chi-square statistic: 74796.00000000001 P-value: 0.24432170848750906 Chi-square test between Longitude and p00u_75 Chi-square statistic: 74796.00000000001 P-value: 0.24432170848750906 Chi-square test between Longitude and p10u_75 Chi-square statistic: 74796.00000000001 P-value: 0.24432170848750906 Chi-square test between Longitude and p90r_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00r_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p10r_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00_150 Chi-square statistic: 75347.99999999999 P-value: 0.24028111743839922 Chi-square test between Longitude and p10_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90u_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00u_150 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between Longitude and p10u_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90r_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00r_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p10r_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p10_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90u_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00u_600 Chi-square statistic: 75072.0 P-value: 0.2422935108134661 Chi-square test between Longitude and p10u_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90r_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00r_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p10r_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p10_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90u_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00u_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p10u_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p90r_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p00r_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and p10r_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between Longitude and Country_encoded Chi-square statistic: 9108.0 P-value: 0.22970721827216425 Chi-square test between p90_1200 and p00_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90r_1200 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90_1200 and p00r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90_1200 and p00u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90_1200 and p10u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90_1200 and p90r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90_1200 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90_1200 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90_1200 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90_1200 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90_1200 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00u_600 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90_1200 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_1200 and Country_encoded Chi-square statistic: 9108.000000000002 P-value: 0.40144847024690966 Chi-square test between p00_1200 and p10_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p10u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90r_1200 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00_1200 and p00r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p10r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p10_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90u_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_1200 and p00u_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_1200 and p10u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00_1200 and p90r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_1200 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00_1200 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00_1200 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00_1200 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00_1200 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_1200 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_1200 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p10_1200 and p90u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p10u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90r_1200 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10_1200 and p00r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p10r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p10_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90u_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10_1200 and p00u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10_1200 and p10u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10_1200 and p90r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10_1200 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10_1200 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10_1200 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10_1200 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10_1200 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00u_600 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10_1200 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_1200 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90u_1200 and p00u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p10u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90r_1200 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_1200 and p00r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p10r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p10_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90u_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_1200 and p00u_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_1200 and p10u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90u_1200 and p90r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00_75 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90u_1200 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90u_1200 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90u_1200 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90u_1200 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90u_1200 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_1200 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_1200 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p00u_1200 and p10u_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90r_1200 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_1200 and p00r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p10r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p10_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00u_1200 and p00u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00u_1200 and p10u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00u_1200 and p90r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_1200 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00u_1200 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00u_1200 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00u_1200 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00u_1200 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_1200 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_1200 and Country_encoded Chi-square statistic: 9108.000000000002 P-value: 0.40144847024690966 Chi-square test between p10u_1200 and p90r_1200 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10u_1200 and p00r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p10r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p10_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10u_1200 and p00u_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_1200 and p10u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10u_1200 and p90r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00_75 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10u_1200 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10u_1200 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10u_1200 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10u_1200 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10u_1200 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00u_600 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10u_1200 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_1200 and p10r_300 Chi-square statistic: 75899.99999999999 P-value: 0.23948344171998676 Chi-square test between p10u_1200 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90r_1200 and p00r_1200 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p10r_1200 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p10_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90u_30 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p90r_1200 and p00u_30 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p90r_1200 and p10u_30 Chi-square statistic: 75348.00000000001 P-value: 0.2410841445243976 Chi-square test between p90r_1200 and p90r_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00r_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p10r_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00_75 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p90r_1200 and p10_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90u_75 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p90r_1200 and p00u_75 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p90r_1200 and p10u_75 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p90r_1200 and p90r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p10r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p10_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00u_150 Chi-square statistic: 75348.00000000001 P-value: 0.2410841445243976 Chi-square test between p90r_1200 and p10u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p10r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p10_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00u_600 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p90r_1200 and p10u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p10r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p10_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p10u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p90r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p00r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and p10r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_1200 and Country_encoded Chi-square statistic: 9108.000000000002 P-value: 0.31045659585425467 Chi-square test between p00r_1200 and p10r_1200 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p10_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00r_1200 and p00u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00r_1200 and p10u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00r_1200 and p90r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00r_1200 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00r_1200 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00r_1200 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00r_1200 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00r_1200 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00r_1200 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_1200 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p10r_1200 and p90_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p10_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p90u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10r_1200 and p00u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10r_1200 and p10u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10r_1200 and p90r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10r_1200 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10r_1200 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10r_1200 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10r_1200 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10r_1200 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10r_1200 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_1200 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90_30 and p00_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p10_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p90u_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90_30 and p00u_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90_30 and p10u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90_30 and p90r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p00_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90_30 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90_30 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90_30 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90_30 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90_30 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p00u_600 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90_30 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_30 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p00_30 and p10_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p90u_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_30 and p00u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00_30 and p10u_30 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00_30 and p90r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p00_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_30 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00_30 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00_30 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00_30 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00_30 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_30 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_30 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p10_30 and p90u_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10_30 and p00u_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10_30 and p10u_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10_30 and p90r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p00_75 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10_30 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10_30 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10_30 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10_30 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10_30 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10_30 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_30 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90u_30 and p00u_30 Chi-square statistic: 75624.0 P-value: 0.0789004181855964 Chi-square test between p90u_30 and p10u_30 Chi-square statistic: 75624.0 P-value: 0.0789004181855964 Chi-square test between p90u_30 and p90r_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p00r_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p10r_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p90_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p00_75 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p90u_30 and p10_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p90u_75 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p90u_30 and p00u_75 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p90u_30 and p10u_75 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p90u_30 and p90r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p00r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p10r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p90_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p00_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p10_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p90u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p00u_150 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p90u_30 and p10u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p90r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p00r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p10r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p90_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p00_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p10_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p90u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p00u_600 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p90u_30 and p10u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p90r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p00r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p10r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p90_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p00_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p10_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p90u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p00u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p10u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p90r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p00r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and p10r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_30 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.31045659585425944 Chi-square test between p00u_30 and p10u_30 Chi-square statistic: 75624.00000000001 P-value: 0.07890041818559086 Chi-square test between p00u_30 and p90r_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p00r_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p10r_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p90_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p00_75 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p00u_30 and p10_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p90u_75 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p00u_30 and p00u_75 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p00u_30 and p10u_75 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p00u_30 and p90r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p00r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p10r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p90_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p00_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p10_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p90u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p00u_150 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p00u_30 and p10u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p90r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p00r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p10r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p90_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p00_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p10_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p90u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p00u_600 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p00u_30 and p10u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p90r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p00r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p10r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p90_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p00_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p10_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p90u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p00u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p10u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p90r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p00r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and p10r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_30 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.31045659585425944 Chi-square test between p10u_30 and p90r_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p00r_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p10r_30 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p90_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p00_75 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p10u_30 and p10_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p90u_75 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p10u_30 and p00u_75 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p10u_30 and p10u_75 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p10u_30 and p90r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p00r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p10r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p90_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p00_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p10_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p90u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p00u_150 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p10u_30 and p10u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p90r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p00r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p10r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p90_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p00_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p10_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p90u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p00u_600 Chi-square statistic: 75348.00000000001 P-value: 0.2410841445243976 Chi-square test between p10u_30 and p10u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p90r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p00r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p10r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p90_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p00_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p10_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p90u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p00u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p10u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p90r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p00r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and p10r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_30 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.31045659585425944 Chi-square test between p90r_30 and p00r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p00_75 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90r_30 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90r_30 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90r_30 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90r_30 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90r_30 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90r_30 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_30 and Country_encoded Chi-square statistic: 9107.999999999998 P-value: 0.40144847024692004 Chi-square test between p00r_30 and p10r_30 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p00_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00r_30 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00r_30 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00r_30 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p00r_30 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00r_30 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00r_30 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_30 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p10r_30 and p90_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p00_75 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10r_30 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10r_30 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10r_30 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10r_30 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p00u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10r_30 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10r_30 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_30 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90_75 and p00_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90_75 and p10_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90_75 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90_75 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p90_75 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90_75 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90_75 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_75 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p00_75 and p10_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.0792797328485861 Chi-square test between p00_75 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.0792797328485861 Chi-square test between p00_75 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.0792797328485861 Chi-square test between p00_75 and p90r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p00r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p10r_75 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p90_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p00_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p10_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p90u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p00u_150 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p00_75 and p10u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p90r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p00r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p10r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p90_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p00_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p10_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p90u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p00u_600 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p00_75 and p10u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p90r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p00r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p10r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p90_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p00_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p10_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p90u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p00u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p10u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p90r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p00r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and p10r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_75 and Country_encoded Chi-square statistic: 9108.000000000002 P-value: 0.31045659585425467 Chi-square test between p10_75 and p90u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10_75 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10_75 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.24028111743837594 Chi-square test between p10_75 and p90r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10_75 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10_75 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_75 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90u_75 and p00u_75 Chi-square statistic: 75348.00000000001 P-value: 0.017201147564985513 Chi-square test between p90u_75 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.017201147564985513 Chi-square test between p90u_75 and p90r_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p00r_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p10r_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p90_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p00_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p10_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p90u_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p00u_150 Chi-square statistic: 75072.0 P-value: 0.2422935108134661 Chi-square test between p90u_75 and p10u_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p90r_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p00r_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p10r_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p90_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p00_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p10_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p90u_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p00u_600 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p90u_75 and p10u_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p90r_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p00r_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p10r_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p90_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p00_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p10_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p90u_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p00u_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p10u_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p90r_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p00r_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and p10r_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p90u_75 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.22970721827216425 Chi-square test between p00u_75 and p10u_75 Chi-square statistic: 75348.00000000001 P-value: 0.017201147564985513 Chi-square test between p00u_75 and p90r_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p00r_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p10r_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p90_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p00_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p10_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p90u_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p00u_150 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p00u_75 and p10u_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p90r_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p00r_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p10r_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p90_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p00_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p10_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p90u_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p00u_600 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p00u_75 and p10u_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p90r_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p00r_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p10r_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p90_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p00_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p10_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p90u_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p00u_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p10u_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p90r_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p00r_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and p10r_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p00u_75 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.22970721827216425 Chi-square test between p10u_75 and p90r_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p00r_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p10r_75 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p90_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p00_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p10_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p90u_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p00u_150 Chi-square statistic: 75072.00000000001 P-value: 0.2422935108134544 Chi-square test between p10u_75 and p10u_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p90r_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p00r_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p10r_150 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p90_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p00_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p10_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p90u_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p00u_600 Chi-square statistic: 75072.0 P-value: 0.2422935108134661 Chi-square test between p10u_75 and p10u_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p90r_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p00r_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p10r_600 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p90_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p00_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p10_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p90u_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p00u_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p10u_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p90r_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p00r_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and p10r_300 Chi-square statistic: 75348.0 P-value: 0.24028111743838765 Chi-square test between p10u_75 and Country_encoded Chi-square statistic: 9108.000000000002 P-value: 0.22970721827216012 Chi-square test between p90r_75 and p00r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90r_75 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p00u_600 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90r_75 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_75 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p00r_75 and p10r_75 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p00u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00r_75 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00r_75 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_75 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p10r_75 and p90_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p10r_75 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10r_75 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_75 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90_150 and p00_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90_150 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90_150 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_150 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p00_150 and p10_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p00u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_150 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p00u_600 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p00_150 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_150 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p10_150 and p90u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p00u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10_150 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10_150 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_150 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90u_150 and p00u_150 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90u_150 and p10u_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_150 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_150 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p00u_150 and p10u_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p90r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p00r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p10r_150 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p90_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p00_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p10_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p90u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p00u_600 Chi-square statistic: 75348.0 P-value: 0.24108414452440924 Chi-square test between p00u_150 and p10u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p90r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p00r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p10r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p90_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p00_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p10_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p90u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p00u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p10u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p90r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p00r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and p10r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_150 and Country_encoded Chi-square statistic: 9078.776470588236 P-value: 0.3904815408394916 Chi-square test between p10u_150 and p90r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10u_150 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_150 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90r_150 and p00r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p00u_600 Chi-square statistic: 75624.00000000001 P-value: 0.23988173660144724 Chi-square test between p90r_150 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_150 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p00r_150 and p10r_150 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00r_150 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_150 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p10r_150 and p90_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10r_150 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_150 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90_600 and p00_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90_600 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_600 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p00_600 and p10_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00_600 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_600 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p10_600 and p90u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p10_600 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_600 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90u_600 and p00u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p90u_600 and p10u_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_600 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p00u_600 and p10u_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and p90r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and p00r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and p10r_600 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and p90_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and p00_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and p10_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and p90u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and p00u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and p10u_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and p90r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and p00r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and p10r_300 Chi-square statistic: 75624.0 P-value: 0.23988173660145884 Chi-square test between p00u_600 and Country_encoded Chi-square statistic: 9096.519327731094 P-value: 0.34106391374156725 Chi-square test between p10u_600 and p90r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_600 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_600 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_600 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_600 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_600 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_600 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_600 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_600 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_600 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_600 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_600 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_600 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90r_600 and p00r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_600 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_600 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_600 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_600 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_600 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_600 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_600 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_600 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_600 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_600 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_600 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p00r_600 and p10r_600 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_600 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_600 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_600 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_600 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_600 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_600 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_600 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_600 and p00r_300 Chi-square statistic: 75899.99999999999 P-value: 0.23948344171998676 Chi-square test between p00r_600 and p10r_300 Chi-square statistic: 75899.99999999999 P-value: 0.23948344171998676 Chi-square test between p00r_600 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p10r_600 and p90_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_600 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_600 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_600 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_600 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_600 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_600 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_600 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_600 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10r_600 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90_300 and p00_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_300 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_300 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_300 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_300 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_300 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_300 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_300 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90_300 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p00_300 and p10_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_300 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_300 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_300 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_300 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_300 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_300 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00_300 and Country_encoded Chi-square statistic: 9108.000000000002 P-value: 0.40144847024690966 Chi-square test between p10_300 and p90u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_300 and p00u_300 Chi-square statistic: 75899.99999999999 P-value: 0.23948344171998676 Chi-square test between p10_300 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_300 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_300 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_300 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10_300 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90u_300 and p00u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_300 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_300 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_300 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_300 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90u_300 and Country_encoded Chi-square statistic: 9108.000000000002 P-value: 0.40144847024690966 Chi-square test between p00u_300 and p10u_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_300 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_300 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_300 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00u_300 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p10u_300 and p90r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_300 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_300 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p10u_300 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p90r_300 and p00r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_300 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p90r_300 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p00r_300 and p10r_300 Chi-square statistic: 75900.0 P-value: 0.2394834417199751 Chi-square test between p00r_300 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149 Chi-square test between p10r_300 and Country_encoded Chi-square statistic: 9108.0 P-value: 0.4014484702469149
# Specify the list of features for the chi-square tests
features = ['Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300', 'Country_encoded']
# Perform the chi-square test for independence
def perform_chi_square_tests(data):
results = []
columns = data.columns
for col1, col2 in combinations(columns, 2):
contingency_table = pd.crosstab(data[col1], data[col2])
chi2, p_value, _, _ = chi2_contingency(contingency_table)
results.append((col1, col2, chi2, p_value))
return results
# Perform the chi-square tests for independence
chi_square_results = perform_chi_square_tests(df)
# Sort the results based on p-values in ascending order
chi_square_results.sort(key=lambda x: x[3])
# Identify the independent and dependent variables based on p-values
independent_variables = []
dependent_variables = []
for result in chi_square_results:
feature1, feature2, _, p_value = result
if p_value < 0.05: # Set the desired significance level
dependent_variables.append((feature1, feature2))
else:
independent_variables.append((feature1, feature2))
# Print the independent and dependent variables
print("Independent variables:")
for pair in independent_variables:
print(pair[0], "and", pair[1])
print("\nDependent variables:")
for pair in dependent_variables:
print(pair[0], "and", pair[1])
Independent variables: p00u_30 and p10u_30 p90u_30 and p00u_30 p90u_30 and p10u_30 p00_75 and p90u_75 p00_75 and p00u_75 p00_75 and p10u_75 p10u_75 and Country_encoded Longitude and Country_encoded p90u_75 and Country_encoded p00u_75 and Country_encoded Latitude and p90_1200 Latitude and p00_1200 Latitude and p10_1200 Latitude and p90u_1200 Latitude and p00u_1200 Latitude and p10u_1200 Latitude and p00r_1200 Latitude and p10r_1200 Latitude and p90_30 Latitude and p00_30 Latitude and p10_30 Latitude and p90r_30 Latitude and p00r_30 Latitude and p10r_30 Latitude and p90_75 Latitude and p10_75 Latitude and p90r_75 Latitude and p00r_75 Latitude and p10r_75 Latitude and p90_150 Latitude and p00_150 Latitude and p10_150 Latitude and p90u_150 Latitude and p10u_150 Latitude and p90r_150 Latitude and p00r_150 Latitude and p10r_150 Latitude and p90_600 Latitude and p00_600 Latitude and p10_600 Latitude and p90u_600 Latitude and p10u_600 Latitude and p90r_600 Latitude and p00r_600 Latitude and p10r_600 Latitude and p90_300 Latitude and p00_300 Latitude and p10_300 Latitude and p90u_300 Latitude and p00u_300 Latitude and p10u_300 Latitude and p90r_300 Latitude and p00r_300 Latitude and p10r_300 p90_1200 and p00_1200 p90_1200 and p10_1200 p90_1200 and p90u_1200 p90_1200 and p00u_1200 p90_1200 and p10u_1200 p90_1200 and p00r_1200 p90_1200 and p10r_1200 p90_1200 and p90_30 p90_1200 and p00_30 p90_1200 and p10_30 p90_1200 and p90r_30 p90_1200 and p00r_30 p90_1200 and p10r_30 p90_1200 and p90_75 p90_1200 and p10_75 p90_1200 and p90r_75 p90_1200 and p00r_75 p90_1200 and p10r_75 p90_1200 and p90_150 p90_1200 and p00_150 p90_1200 and p10_150 p90_1200 and p90u_150 p90_1200 and p10u_150 p90_1200 and p90r_150 p90_1200 and p00r_150 p90_1200 and p10r_150 p90_1200 and p90_600 p90_1200 and p00_600 p90_1200 and p10_600 p90_1200 and p90u_600 p90_1200 and p10u_600 p90_1200 and p90r_600 p90_1200 and p00r_600 p90_1200 and p10r_600 p90_1200 and p90_300 p90_1200 and p00_300 p90_1200 and p10_300 p90_1200 and p90u_300 p90_1200 and p00u_300 p90_1200 and p10u_300 p90_1200 and p90r_300 p90_1200 and p00r_300 p90_1200 and p10r_300 p00_1200 and p10_1200 p00_1200 and p90u_1200 p00_1200 and p00u_1200 p00_1200 and p10u_1200 p00_1200 and p00r_1200 p00_1200 and p10r_1200 p00_1200 and p90_30 p00_1200 and p00_30 p00_1200 and p10_30 p00_1200 and p90r_30 p00_1200 and p00r_30 p00_1200 and p10r_30 p00_1200 and p90_75 p00_1200 and p10_75 p00_1200 and p90r_75 p00_1200 and p00r_75 p00_1200 and p10r_75 p00_1200 and p90_150 p00_1200 and p00_150 p00_1200 and p10_150 p00_1200 and p90u_150 p00_1200 and p10u_150 p00_1200 and p90r_150 p00_1200 and p00r_150 p00_1200 and p10r_150 p00_1200 and p90_600 p00_1200 and p00_600 p00_1200 and p10_600 p00_1200 and p90u_600 p00_1200 and p10u_600 p00_1200 and p90r_600 p00_1200 and p00r_600 p00_1200 and p10r_600 p00_1200 and p90_300 p00_1200 and p00_300 p00_1200 and p10_300 p00_1200 and p90u_300 p00_1200 and p00u_300 p00_1200 and p10u_300 p00_1200 and p90r_300 p00_1200 and p00r_300 p00_1200 and p10r_300 p10_1200 and p90u_1200 p10_1200 and p00u_1200 p10_1200 and p10u_1200 p10_1200 and p00r_1200 p10_1200 and p10r_1200 p10_1200 and p90_30 p10_1200 and p00_30 p10_1200 and p10_30 p10_1200 and p90r_30 p10_1200 and p00r_30 p10_1200 and p10r_30 p10_1200 and p90_75 p10_1200 and p10_75 p10_1200 and p90r_75 p10_1200 and p00r_75 p10_1200 and p10r_75 p10_1200 and p90_150 p10_1200 and p00_150 p10_1200 and p10_150 p10_1200 and p90u_150 p10_1200 and p10u_150 p10_1200 and p90r_150 p10_1200 and p00r_150 p10_1200 and p10r_150 p10_1200 and p90_600 p10_1200 and p00_600 p10_1200 and p10_600 p10_1200 and p90u_600 p10_1200 and p10u_600 p10_1200 and p90r_600 p10_1200 and p00r_600 p10_1200 and p10r_600 p10_1200 and p90_300 p10_1200 and p00_300 p10_1200 and p10_300 p10_1200 and p90u_300 p10_1200 and p00u_300 p10_1200 and p10u_300 p10_1200 and p90r_300 p10_1200 and p00r_300 p10_1200 and p10r_300 p90u_1200 and p00u_1200 p90u_1200 and p10u_1200 p90u_1200 and p00r_1200 p90u_1200 and p10r_1200 p90u_1200 and p90_30 p90u_1200 and p00_30 p90u_1200 and p10_30 p90u_1200 and p90r_30 p90u_1200 and p00r_30 p90u_1200 and p10r_30 p90u_1200 and p90_75 p90u_1200 and p10_75 p90u_1200 and p90r_75 p90u_1200 and p00r_75 p90u_1200 and p10r_75 p90u_1200 and p90_150 p90u_1200 and p00_150 p90u_1200 and p10_150 p90u_1200 and p90u_150 p90u_1200 and p10u_150 p90u_1200 and p90r_150 p90u_1200 and p00r_150 p90u_1200 and p10r_150 p90u_1200 and p90_600 p90u_1200 and p00_600 p90u_1200 and p10_600 p90u_1200 and p90u_600 p90u_1200 and p10u_600 p90u_1200 and p90r_600 p90u_1200 and p00r_600 p90u_1200 and p10r_600 p90u_1200 and p90_300 p90u_1200 and p00_300 p90u_1200 and p10_300 p90u_1200 and p90u_300 p90u_1200 and p00u_300 p90u_1200 and p10u_300 p90u_1200 and p90r_300 p90u_1200 and p00r_300 p90u_1200 and p10r_300 p00u_1200 and p10u_1200 p00u_1200 and p00r_1200 p00u_1200 and p10r_1200 p00u_1200 and p90_30 p00u_1200 and p00_30 p00u_1200 and p10_30 p00u_1200 and p90r_30 p00u_1200 and p00r_30 p00u_1200 and p10r_30 p00u_1200 and p90_75 p00u_1200 and p10_75 p00u_1200 and p90r_75 p00u_1200 and p00r_75 p00u_1200 and p10r_75 p00u_1200 and p90_150 p00u_1200 and p00_150 p00u_1200 and p10_150 p00u_1200 and p90u_150 p00u_1200 and p10u_150 p00u_1200 and p90r_150 p00u_1200 and p00r_150 p00u_1200 and p10r_150 p00u_1200 and p90_600 p00u_1200 and p00_600 p00u_1200 and p10_600 p00u_1200 and p90u_600 p00u_1200 and p10u_600 p00u_1200 and p90r_600 p00u_1200 and p00r_600 p00u_1200 and p10r_600 p00u_1200 and p90_300 p00u_1200 and p00_300 p00u_1200 and p10_300 p00u_1200 and p90u_300 p00u_1200 and p00u_300 p00u_1200 and p10u_300 p00u_1200 and p90r_300 p00u_1200 and p00r_300 p00u_1200 and p10r_300 p10u_1200 and p00r_1200 p10u_1200 and p10r_1200 p10u_1200 and p90_30 p10u_1200 and p00_30 p10u_1200 and p10_30 p10u_1200 and p90r_30 p10u_1200 and p00r_30 p10u_1200 and p10r_30 p10u_1200 and p90_75 p10u_1200 and p10_75 p10u_1200 and p90r_75 p10u_1200 and p00r_75 p10u_1200 and p10r_75 p10u_1200 and p90_150 p10u_1200 and p00_150 p10u_1200 and p10_150 p10u_1200 and p90u_150 p10u_1200 and p10u_150 p10u_1200 and p90r_150 p10u_1200 and p00r_150 p10u_1200 and p10r_150 p10u_1200 and p90_600 p10u_1200 and p00_600 p10u_1200 and p10_600 p10u_1200 and p90u_600 p10u_1200 and p10u_600 p10u_1200 and p90r_600 p10u_1200 and p00r_600 p10u_1200 and p10r_600 p10u_1200 and p90_300 p10u_1200 and p00_300 p10u_1200 and p10_300 p10u_1200 and p90u_300 p10u_1200 and p00u_300 p10u_1200 and p10u_300 p10u_1200 and p90r_300 p10u_1200 and p00r_300 p00r_1200 and p10r_1200 p00r_1200 and p90_30 p00r_1200 and p00_30 p00r_1200 and p10_30 p00r_1200 and p90r_30 p00r_1200 and p00r_30 p00r_1200 and p10r_30 p00r_1200 and p90_75 p00r_1200 and p10_75 p00r_1200 and p90r_75 p00r_1200 and p00r_75 p00r_1200 and p10r_75 p00r_1200 and p90_150 p00r_1200 and p00_150 p00r_1200 and p10_150 p00r_1200 and p90u_150 p00r_1200 and p10u_150 p00r_1200 and p90r_150 p00r_1200 and p00r_150 p00r_1200 and p10r_150 p00r_1200 and p90_600 p00r_1200 and p00_600 p00r_1200 and p10_600 p00r_1200 and p90u_600 p00r_1200 and p10u_600 p00r_1200 and p90r_600 p00r_1200 and p00r_600 p00r_1200 and p10r_600 p00r_1200 and p90_300 p00r_1200 and p00_300 p00r_1200 and p10_300 p00r_1200 and p90u_300 p00r_1200 and p00u_300 p00r_1200 and p10u_300 p00r_1200 and p90r_300 p00r_1200 and p00r_300 p00r_1200 and p10r_300 p10r_1200 and p90_30 p10r_1200 and p00_30 p10r_1200 and p10_30 p10r_1200 and p90r_30 p10r_1200 and p00r_30 p10r_1200 and p10r_30 p10r_1200 and p90_75 p10r_1200 and p10_75 p10r_1200 and p90r_75 p10r_1200 and p00r_75 p10r_1200 and p10r_75 p10r_1200 and p90_150 p10r_1200 and p00_150 p10r_1200 and p10_150 p10r_1200 and p90u_150 p10r_1200 and p10u_150 p10r_1200 and p90r_150 p10r_1200 and p00r_150 p10r_1200 and p10r_150 p10r_1200 and p90_600 p10r_1200 and p00_600 p10r_1200 and p10_600 p10r_1200 and p90u_600 p10r_1200 and p10u_600 p10r_1200 and p90r_600 p10r_1200 and p00r_600 p10r_1200 and p10r_600 p10r_1200 and p90_300 p10r_1200 and p00_300 p10r_1200 and p10_300 p10r_1200 and p90u_300 p10r_1200 and p00u_300 p10r_1200 and p10u_300 p10r_1200 and p90r_300 p10r_1200 and p00r_300 p10r_1200 and p10r_300 p90_30 and p00_30 p90_30 and p10_30 p90_30 and p90r_30 p90_30 and p00r_30 p90_30 and p10r_30 p90_30 and p90_75 p90_30 and p10_75 p90_30 and p90r_75 p90_30 and p00r_75 p90_30 and p10r_75 p90_30 and p90_150 p90_30 and p00_150 p90_30 and p10_150 p90_30 and p90u_150 p90_30 and p10u_150 p90_30 and p90r_150 p90_30 and p00r_150 p90_30 and p10r_150 p90_30 and p90_600 p90_30 and p00_600 p90_30 and p10_600 p90_30 and p90u_600 p90_30 and p10u_600 p90_30 and p90r_600 p90_30 and p00r_600 p90_30 and p10r_600 p90_30 and p90_300 p90_30 and p00_300 p90_30 and p10_300 p90_30 and p90u_300 p90_30 and p00u_300 p90_30 and p10u_300 p90_30 and p90r_300 p90_30 and p00r_300 p90_30 and p10r_300 p00_30 and p10_30 p00_30 and p90r_30 p00_30 and p00r_30 p00_30 and p10r_30 p00_30 and p90_75 p00_30 and p10_75 p00_30 and p90r_75 p00_30 and p00r_75 p00_30 and p10r_75 p00_30 and p90_150 p00_30 and p00_150 p00_30 and p10_150 p00_30 and p90u_150 p00_30 and p10u_150 p00_30 and p90r_150 p00_30 and p00r_150 p00_30 and p10r_150 p00_30 and p90_600 p00_30 and p00_600 p00_30 and p10_600 p00_30 and p90u_600 p00_30 and p10u_600 p00_30 and p90r_600 p00_30 and p00r_600 p00_30 and p10r_600 p00_30 and p90_300 p00_30 and p00_300 p00_30 and p10_300 p00_30 and p90u_300 p00_30 and p00u_300 p00_30 and p10u_300 p00_30 and p90r_300 p00_30 and p00r_300 p00_30 and p10r_300 p10_30 and p90r_30 p10_30 and p00r_30 p10_30 and p10r_30 p10_30 and p90_75 p10_30 and p10_75 p10_30 and p90r_75 p10_30 and p00r_75 p10_30 and p10r_75 p10_30 and p90_150 p10_30 and p00_150 p10_30 and p10_150 p10_30 and p90u_150 p10_30 and p10u_150 p10_30 and p90r_150 p10_30 and p00r_150 p10_30 and p10r_150 p10_30 and p90_600 p10_30 and p00_600 p10_30 and p10_600 p10_30 and p90u_600 p10_30 and p10u_600 p10_30 and p90r_600 p10_30 and p00r_600 p10_30 and p10r_600 p10_30 and p90_300 p10_30 and p00_300 p10_30 and p10_300 p10_30 and p90u_300 p10_30 and p00u_300 p10_30 and p10u_300 p10_30 and p90r_300 p10_30 and p00r_300 p10_30 and p10r_300 p90r_30 and p00r_30 p90r_30 and p10r_30 p90r_30 and p90_75 p90r_30 and p10_75 p90r_30 and p90r_75 p90r_30 and p00r_75 p90r_30 and p10r_75 p90r_30 and p90_150 p90r_30 and p00_150 p90r_30 and p10_150 p90r_30 and p90u_150 p90r_30 and p10u_150 p90r_30 and p90r_150 p90r_30 and p00r_150 p90r_30 and p10r_150 p90r_30 and p90_600 p90r_30 and p00_600 p90r_30 and p10_600 p90r_30 and p90u_600 p90r_30 and p10u_600 p90r_30 and p90r_600 p90r_30 and p00r_600 p90r_30 and p10r_600 p90r_30 and p90_300 p90r_30 and p00_300 p90r_30 and p10_300 p90r_30 and p90u_300 p90r_30 and p00u_300 p90r_30 and p10u_300 p90r_30 and p90r_300 p90r_30 and p00r_300 p90r_30 and p10r_300 p00r_30 and p10r_30 p00r_30 and p90_75 p00r_30 and p10_75 p00r_30 and p90r_75 p00r_30 and p00r_75 p00r_30 and p10r_75 p00r_30 and p90_150 p00r_30 and p00_150 p00r_30 and p10_150 p00r_30 and p90u_150 p00r_30 and p10u_150 p00r_30 and p90r_150 p00r_30 and p00r_150 p00r_30 and p10r_150 p00r_30 and p90_600 p00r_30 and p00_600 p00r_30 and p10_600 p00r_30 and p90u_600 p00r_30 and p10u_600 p00r_30 and p90r_600 p00r_30 and p00r_600 p00r_30 and p10r_600 p00r_30 and p90_300 p00r_30 and p00_300 p00r_30 and p10_300 p00r_30 and p90u_300 p00r_30 and p00u_300 p00r_30 and p10u_300 p00r_30 and p90r_300 p00r_30 and p00r_300 p00r_30 and p10r_300 p10r_30 and p90_75 p10r_30 and p10_75 p10r_30 and p90r_75 p10r_30 and p00r_75 p10r_30 and p10r_75 p10r_30 and p90_150 p10r_30 and p00_150 p10r_30 and p10_150 p10r_30 and p90u_150 p10r_30 and p10u_150 p10r_30 and p90r_150 p10r_30 and p00r_150 p10r_30 and p10r_150 p10r_30 and p90_600 p10r_30 and p00_600 p10r_30 and p10_600 p10r_30 and p90u_600 p10r_30 and p10u_600 p10r_30 and p90r_600 p10r_30 and p00r_600 p10r_30 and p10r_600 p10r_30 and p90_300 p10r_30 and p00_300 p10r_30 and p10_300 p10r_30 and p90u_300 p10r_30 and p00u_300 p10r_30 and p10u_300 p10r_30 and p90r_300 p10r_30 and p00r_300 p10r_30 and p10r_300 p90_75 and p10_75 p90_75 and p90r_75 p90_75 and p00r_75 p90_75 and p10r_75 p90_75 and p90_150 p90_75 and p00_150 p90_75 and p10_150 p90_75 and p90u_150 p90_75 and p10u_150 p90_75 and p90r_150 p90_75 and p00r_150 p90_75 and p10r_150 p90_75 and p90_600 p90_75 and p00_600 p90_75 and p10_600 p90_75 and p90u_600 p90_75 and p10u_600 p90_75 and p90r_600 p90_75 and p00r_600 p90_75 and p10r_600 p90_75 and p90_300 p90_75 and p00_300 p90_75 and p10_300 p90_75 and p90u_300 p90_75 and p00u_300 p90_75 and p10u_300 p90_75 and p90r_300 p90_75 and p00r_300 p90_75 and p10r_300 p10_75 and p90r_75 p10_75 and p00r_75 p10_75 and p10r_75 p10_75 and p90_150 p10_75 and p00_150 p10_75 and p10_150 p10_75 and p90u_150 p10_75 and p10u_150 p10_75 and p90r_150 p10_75 and p00r_150 p10_75 and p10r_150 p10_75 and p90_600 p10_75 and p00_600 p10_75 and p10_600 p10_75 and p90u_600 p10_75 and p10u_600 p10_75 and p90r_600 p10_75 and p00r_600 p10_75 and p10r_600 p10_75 and p90_300 p10_75 and p00_300 p10_75 and p10_300 p10_75 and p90u_300 p10_75 and p00u_300 p10_75 and p10u_300 p10_75 and p90r_300 p10_75 and p00r_300 p10_75 and p10r_300 p90r_75 and p00r_75 p90r_75 and p10r_75 p90r_75 and p90_150 p90r_75 and p00_150 p90r_75 and p10_150 p90r_75 and p90u_150 p90r_75 and p10u_150 p90r_75 and p90r_150 p90r_75 and p00r_150 p90r_75 and p10r_150 p90r_75 and p90_600 p90r_75 and p00_600 p90r_75 and p10_600 p90r_75 and p90u_600 p90r_75 and p10u_600 p90r_75 and p90r_600 p90r_75 and p00r_600 p90r_75 and p10r_600 p90r_75 and p90_300 p90r_75 and p00_300 p90r_75 and p10_300 p90r_75 and p90u_300 p90r_75 and p00u_300 p90r_75 and p10u_300 p90r_75 and p90r_300 p90r_75 and p00r_300 p90r_75 and p10r_300 p00r_75 and p10r_75 p00r_75 and p90_150 p00r_75 and p00_150 p00r_75 and p10_150 p00r_75 and p90u_150 p00r_75 and p10u_150 p00r_75 and p90r_150 p00r_75 and p00r_150 p00r_75 and p10r_150 p00r_75 and p90_600 p00r_75 and p00_600 p00r_75 and p10_600 p00r_75 and p90u_600 p00r_75 and p10u_600 p00r_75 and p90r_600 p00r_75 and p00r_600 p00r_75 and p10r_600 p00r_75 and p90_300 p00r_75 and p00_300 p00r_75 and p10_300 p00r_75 and p90u_300 p00r_75 and p00u_300 p00r_75 and p10u_300 p00r_75 and p90r_300 p00r_75 and p00r_300 p00r_75 and p10r_300 p10r_75 and p90_150 p10r_75 and p00_150 p10r_75 and p10_150 p10r_75 and p90u_150 p10r_75 and p10u_150 p10r_75 and p90r_150 p10r_75 and p00r_150 p10r_75 and p10r_150 p10r_75 and p90_600 p10r_75 and p00_600 p10r_75 and p10_600 p10r_75 and p90u_600 p10r_75 and p10u_600 p10r_75 and p90r_600 p10r_75 and p00r_600 p10r_75 and p10r_600 p10r_75 and p90_300 p10r_75 and p00_300 p10r_75 and p10_300 p10r_75 and p90u_300 p10r_75 and p00u_300 p10r_75 and p10u_300 p10r_75 and p90r_300 p10r_75 and p00r_300 p10r_75 and p10r_300 p90_150 and p00_150 p90_150 and p10_150 p90_150 and p90u_150 p90_150 and p10u_150 p90_150 and p90r_150 p90_150 and p00r_150 p90_150 and p10r_150 p90_150 and p90_600 p90_150 and p00_600 p90_150 and p10_600 p90_150 and p90u_600 p90_150 and p10u_600 p90_150 and p90r_600 p90_150 and p00r_600 p90_150 and p10r_600 p90_150 and p90_300 p90_150 and p00_300 p90_150 and p10_300 p90_150 and p90u_300 p90_150 and p00u_300 p90_150 and p10u_300 p90_150 and p90r_300 p90_150 and p00r_300 p90_150 and p10r_300 p00_150 and p10_150 p00_150 and p90u_150 p00_150 and p10u_150 p00_150 and p90r_150 p00_150 and p00r_150 p00_150 and p10r_150 p00_150 and p90_600 p00_150 and p00_600 p00_150 and p10_600 p00_150 and p90u_600 p00_150 and p10u_600 p00_150 and p90r_600 p00_150 and p00r_600 p00_150 and p10r_600 p00_150 and p90_300 p00_150 and p00_300 p00_150 and p10_300 p00_150 and p90u_300 p00_150 and p00u_300 p00_150 and p10u_300 p00_150 and p90r_300 p00_150 and p00r_300 p00_150 and p10r_300 p10_150 and p90u_150 p10_150 and p10u_150 p10_150 and p90r_150 p10_150 and p00r_150 p10_150 and p10r_150 p10_150 and p90_600 p10_150 and p00_600 p10_150 and p10_600 p10_150 and p90u_600 p10_150 and p10u_600 p10_150 and p90r_600 p10_150 and p00r_600 p10_150 and p10r_600 p10_150 and p90_300 p10_150 and p00_300 p10_150 and p10_300 p10_150 and p90u_300 p10_150 and p00u_300 p10_150 and p10u_300 p10_150 and p90r_300 p10_150 and p00r_300 p10_150 and p10r_300 p90u_150 and p10u_150 p90u_150 and p90r_150 p90u_150 and p00r_150 p90u_150 and p10r_150 p90u_150 and p90_600 p90u_150 and p00_600 p90u_150 and p10_600 p90u_150 and p90u_600 p90u_150 and p10u_600 p90u_150 and p90r_600 p90u_150 and p00r_600 p90u_150 and p10r_600 p90u_150 and p90_300 p90u_150 and p00_300 p90u_150 and p10_300 p90u_150 and p90u_300 p90u_150 and p00u_300 p90u_150 and p10u_300 p90u_150 and p90r_300 p90u_150 and p00r_300 p90u_150 and p10r_300 p10u_150 and p90r_150 p10u_150 and p00r_150 p10u_150 and p10r_150 p10u_150 and p90_600 p10u_150 and p00_600 p10u_150 and p10_600 p10u_150 and p90u_600 p10u_150 and p10u_600 p10u_150 and p90r_600 p10u_150 and p00r_600 p10u_150 and p10r_600 p10u_150 and p90_300 p10u_150 and p00_300 p10u_150 and p10_300 p10u_150 and p90u_300 p10u_150 and p00u_300 p10u_150 and p10u_300 p10u_150 and p90r_300 p10u_150 and p00r_300 p10u_150 and p10r_300 p90r_150 and p00r_150 p90r_150 and p10r_150 p90r_150 and p90_600 p90r_150 and p00_600 p90r_150 and p10_600 p90r_150 and p90u_600 p90r_150 and p10u_600 p90r_150 and p90r_600 p90r_150 and p00r_600 p90r_150 and p10r_600 p90r_150 and p90_300 p90r_150 and p00_300 p90r_150 and p10_300 p90r_150 and p90u_300 p90r_150 and p00u_300 p90r_150 and p10u_300 p90r_150 and p90r_300 p90r_150 and p00r_300 p90r_150 and p10r_300 p00r_150 and p10r_150 p00r_150 and p90_600 p00r_150 and p00_600 p00r_150 and p10_600 p00r_150 and p90u_600 p00r_150 and p10u_600 p00r_150 and p90r_600 p00r_150 and p00r_600 p00r_150 and p10r_600 p00r_150 and p90_300 p00r_150 and p00_300 p00r_150 and p10_300 p00r_150 and p90u_300 p00r_150 and p00u_300 p00r_150 and p10u_300 p00r_150 and p90r_300 p00r_150 and p00r_300 p00r_150 and p10r_300 p10r_150 and p90_600 p10r_150 and p00_600 p10r_150 and p10_600 p10r_150 and p90u_600 p10r_150 and p10u_600 p10r_150 and p90r_600 p10r_150 and p00r_600 p10r_150 and p10r_600 p10r_150 and p90_300 p10r_150 and p00_300 p10r_150 and p10_300 p10r_150 and p90u_300 p10r_150 and p00u_300 p10r_150 and p10u_300 p10r_150 and p90r_300 p10r_150 and p00r_300 p10r_150 and p10r_300 p90_600 and p00_600 p90_600 and p10_600 p90_600 and p90u_600 p90_600 and p10u_600 p90_600 and p90r_600 p90_600 and p00r_600 p90_600 and p10r_600 p90_600 and p90_300 p90_600 and p00_300 p90_600 and p10_300 p90_600 and p90u_300 p90_600 and p00u_300 p90_600 and p10u_300 p90_600 and p90r_300 p90_600 and p00r_300 p90_600 and p10r_300 p00_600 and p10_600 p00_600 and p90u_600 p00_600 and p10u_600 p00_600 and p90r_600 p00_600 and p00r_600 p00_600 and p10r_600 p00_600 and p90_300 p00_600 and p00_300 p00_600 and p10_300 p00_600 and p90u_300 p00_600 and p00u_300 p00_600 and p10u_300 p00_600 and p90r_300 p00_600 and p00r_300 p00_600 and p10r_300 p10_600 and p90u_600 p10_600 and p10u_600 p10_600 and p90r_600 p10_600 and p00r_600 p10_600 and p10r_600 p10_600 and p90_300 p10_600 and p00_300 p10_600 and p10_300 p10_600 and p90u_300 p10_600 and p00u_300 p10_600 and p10u_300 p10_600 and p90r_300 p10_600 and p00r_300 p10_600 and p10r_300 p90u_600 and p10u_600 p90u_600 and p90r_600 p90u_600 and p00r_600 p90u_600 and p10r_600 p90u_600 and p90_300 p90u_600 and p00_300 p90u_600 and p10_300 p90u_600 and p90u_300 p90u_600 and p00u_300 p90u_600 and p10u_300 p90u_600 and p90r_300 p90u_600 and p00r_300 p90u_600 and p10r_300 p10u_600 and p90r_600 p10u_600 and p00r_600 p10u_600 and p10r_600 p10u_600 and p90_300 p10u_600 and p00_300 p10u_600 and p10_300 p10u_600 and p90u_300 p10u_600 and p00u_300 p10u_600 and p10u_300 p10u_600 and p90r_300 p10u_600 and p00r_300 p10u_600 and p10r_300 p90r_600 and p00r_600 p90r_600 and p10r_600 p90r_600 and p90_300 p90r_600 and p00_300 p90r_600 and p10_300 p90r_600 and p90u_300 p90r_600 and p00u_300 p90r_600 and p10u_300 p90r_600 and p90r_300 p90r_600 and p00r_300 p90r_600 and p10r_300 p00r_600 and p10r_600 p00r_600 and p90_300 p00r_600 and p00_300 p00r_600 and p10_300 p00r_600 and p90u_300 p00r_600 and p00u_300 p00r_600 and p10u_300 p00r_600 and p90r_300 p10r_600 and p90_300 p10r_600 and p00_300 p10r_600 and p10_300 p10r_600 and p90u_300 p10r_600 and p00u_300 p10r_600 and p10u_300 p10r_600 and p90r_300 p10r_600 and p00r_300 p10r_600 and p10r_300 p90_300 and p00_300 p90_300 and p10_300 p90_300 and p90u_300 p90_300 and p00u_300 p90_300 and p10u_300 p90_300 and p90r_300 p90_300 and p00r_300 p90_300 and p10r_300 p00_300 and p10_300 p00_300 and p90u_300 p00_300 and p00u_300 p00_300 and p10u_300 p00_300 and p90r_300 p00_300 and p00r_300 p00_300 and p10r_300 p10_300 and p90u_300 p10_300 and p10u_300 p10_300 and p90r_300 p10_300 and p00r_300 p10_300 and p10r_300 p90u_300 and p00u_300 p90u_300 and p10u_300 p90u_300 and p90r_300 p90u_300 and p00r_300 p90u_300 and p10r_300 p00u_300 and p10u_300 p00u_300 and p90r_300 p00u_300 and p00r_300 p00u_300 and p10r_300 p10u_300 and p90r_300 p10u_300 and p00r_300 p10u_300 and p10r_300 p90r_300 and p00r_300 p90r_300 and p10r_300 p00r_300 and p10r_300 p10u_1200 and p10r_300 p00r_600 and p00r_300 p00r_600 and p10r_300 p10_300 and p00u_300 Latitude and p90u_30 Latitude and p00u_30 Latitude and p10u_30 Latitude and p00_75 Latitude and p00u_150 p90_1200 and p90u_30 p90_1200 and p00u_30 p90_1200 and p10u_30 p90_1200 and p00u_600 p00_1200 and p90r_1200 p00_1200 and p10u_30 p00_1200 and p00u_150 p10_1200 and p90r_1200 p10_1200 and p00u_30 p10_1200 and p10u_30 p10_1200 and p00u_150 p10_1200 and p00u_600 p90u_1200 and p10u_30 p90u_1200 and p00_75 p90u_1200 and p00u_150 p00u_1200 and p90u_30 p00u_1200 and p00u_30 p00u_1200 and p10u_30 p00u_1200 and p00u_150 p10u_1200 and p90r_1200 p10u_1200 and p90u_30 p10u_1200 and p10u_30 p10u_1200 and p00_75 p10u_1200 and p00u_150 p10u_1200 and p00u_600 p00r_1200 and p90u_30 p00r_1200 and p00u_30 p00r_1200 and p10u_30 p00r_1200 and p00u_150 p10r_1200 and p90u_30 p10r_1200 and p00u_30 p10r_1200 and p10u_30 p10r_1200 and p00u_150 p90_30 and p10u_30 p90_30 and p00u_150 p90_30 and p00u_600 p00_30 and p00u_30 p00_30 and p10u_30 p00_30 and p00u_150 p10_30 and p00_75 p10_30 and p00u_150 p90r_30 and p00_75 p90r_30 and p00u_150 p00r_30 and p00u_150 p10r_30 and p00_75 p90_75 and p00u_150 p10_75 and p00u_150 p90r_75 and p00u_150 p90r_75 and p00u_600 p10r_75 and p00u_150 p90_150 and p00u_150 p00_150 and p00u_600 p90u_150 and p00u_150 p90r_150 and p00u_600 Latitude and p90r_1200 Latitude and p00u_600 p90_1200 and p90r_1200 p90_1200 and p00_75 p90_1200 and p00u_150 p00_1200 and p90u_30 p00_1200 and p00u_30 p00_1200 and p00_75 p00_1200 and p00u_600 p10_1200 and p90u_30 p10_1200 and p00_75 p90u_1200 and p90r_1200 p90u_1200 and p90u_30 p90u_1200 and p00u_30 p90u_1200 and p00u_600 p00u_1200 and p90r_1200 p00u_1200 and p00_75 p00u_1200 and p00u_600 p10u_1200 and p00u_30 p90r_1200 and p00r_1200 p90r_1200 and p10r_1200 p90r_1200 and p90_30 p90r_1200 and p00_30 p90r_1200 and p10_30 p90r_1200 and p90r_30 p90r_1200 and p00r_30 p90r_1200 and p10r_30 p90r_1200 and p90_75 p90r_1200 and p10_75 p90r_1200 and p90r_75 p90r_1200 and p00r_75 p90r_1200 and p10r_75 p90r_1200 and p90_150 p90r_1200 and p00_150 p90r_1200 and p10_150 p90r_1200 and p90u_150 p90r_1200 and p10u_150 p90r_1200 and p90r_150 p90r_1200 and p00r_150 p90r_1200 and p10r_150 p90r_1200 and p90_600 p90r_1200 and p00_600 p90r_1200 and p10_600 p90r_1200 and p90u_600 p90r_1200 and p10u_600 p90r_1200 and p90r_600 p90r_1200 and p00r_600 p90r_1200 and p10r_600 p90r_1200 and p90_300 p90r_1200 and p00_300 p90r_1200 and p10_300 p90r_1200 and p90u_300 p90r_1200 and p00u_300 p90r_1200 and p10u_300 p90r_1200 and p90r_300 p90r_1200 and p00r_300 p90r_1200 and p10r_300 p00r_1200 and p00_75 p00r_1200 and p00u_600 p10r_1200 and p00_75 p10r_1200 and p00u_600 p90_30 and p90u_30 p90_30 and p00u_30 p90_30 and p00_75 p00_30 and p90u_30 p00_30 and p00_75 p00_30 and p00u_600 p10_30 and p90u_30 p10_30 and p00u_30 p10_30 and p10u_30 p10_30 and p00u_600 p90u_30 and p90r_30 p90u_30 and p00r_30 p90u_30 and p10r_30 p90u_30 and p90_75 p90u_30 and p10_75 p90u_30 and p90r_75 p90u_30 and p00r_75 p90u_30 and p10r_75 p90u_30 and p90_150 p90u_30 and p00_150 p90u_30 and p10_150 p90u_30 and p90u_150 p90u_30 and p10u_150 p90u_30 and p90r_150 p90u_30 and p00r_150 p90u_30 and p10r_150 p90u_30 and p90_600 p90u_30 and p00_600 p90u_30 and p10_600 p90u_30 and p90u_600 p90u_30 and p10u_600 p90u_30 and p90r_600 p90u_30 and p00r_600 p90u_30 and p10r_600 p90u_30 and p90_300 p90u_30 and p00_300 p90u_30 and p10_300 p90u_30 and p90u_300 p90u_30 and p00u_300 p90u_30 and p10u_300 p90u_30 and p90r_300 p90u_30 and p00r_300 p90u_30 and p10r_300 p00u_30 and p90r_30 p00u_30 and p00r_30 p00u_30 and p10r_30 p00u_30 and p90_75 p00u_30 and p10_75 p00u_30 and p90r_75 p00u_30 and p00r_75 p00u_30 and p10r_75 p00u_30 and p90_150 p00u_30 and p00_150 p00u_30 and p10_150 p00u_30 and p90u_150 p00u_30 and p10u_150 p00u_30 and p90r_150 p00u_30 and p00r_150 p00u_30 and p10r_150 p00u_30 and p90_600 p00u_30 and p00_600 p00u_30 and p10_600 p00u_30 and p90u_600 p00u_30 and p10u_600 p00u_30 and p90r_600 p00u_30 and p00r_600 p00u_30 and p10r_600 p00u_30 and p90_300 p00u_30 and p00_300 p00u_30 and p10_300 p00u_30 and p90u_300 p00u_30 and p00u_300 p00u_30 and p10u_300 p00u_30 and p90r_300 p00u_30 and p00r_300 p00u_30 and p10r_300 p10u_30 and p90r_30 p10u_30 and p00r_30 p10u_30 and p10r_30 p10u_30 and p90_75 p10u_30 and p10_75 p10u_30 and p90r_75 p10u_30 and p00r_75 p10u_30 and p10r_75 p10u_30 and p90_150 p10u_30 and p00_150 p10u_30 and p10_150 p10u_30 and p90u_150 p10u_30 and p10u_150 p10u_30 and p90r_150 p10u_30 and p00r_150 p10u_30 and p10r_150 p10u_30 and p90_600 p10u_30 and p00_600 p10u_30 and p10_600 p10u_30 and p90u_600 p10u_30 and p10u_600 p10u_30 and p90r_600 p10u_30 and p00r_600 p10u_30 and p10r_600 p10u_30 and p90_300 p10u_30 and p00_300 p10u_30 and p10_300 p10u_30 and p90u_300 p10u_30 and p00u_300 p10u_30 and p10u_300 p10u_30 and p90r_300 p10u_30 and p00r_300 p10u_30 and p10r_300 p90r_30 and p00u_600 p00r_30 and p00_75 p00r_30 and p00u_600 p10r_30 and p00u_150 p10r_30 and p00u_600 p90_75 and p00_75 p90_75 and p00u_600 p00_75 and p10_75 p00_75 and p90r_75 p00_75 and p00r_75 p00_75 and p10r_75 p00_75 and p90_150 p00_75 and p00_150 p00_75 and p10_150 p00_75 and p90u_150 p00_75 and p10u_150 p00_75 and p90r_150 p00_75 and p00r_150 p00_75 and p10r_150 p00_75 and p90_600 p00_75 and p00_600 p00_75 and p10_600 p00_75 and p90u_600 p00_75 and p10u_600 p00_75 and p90r_600 p00_75 and p00r_600 p00_75 and p10r_600 p00_75 and p90_300 p00_75 and p00_300 p00_75 and p10_300 p00_75 and p90u_300 p00_75 and p00u_300 p00_75 and p10u_300 p00_75 and p90r_300 p00_75 and p00r_300 p00_75 and p10r_300 p10_75 and p00u_600 p00r_75 and p00u_150 p00r_75 and p00u_600 p10r_75 and p00u_600 p90_150 and p00u_600 p00_150 and p00u_150 p10_150 and p00u_150 p10_150 and p00u_600 p90u_150 and p00u_600 p00u_150 and p10u_150 p00u_150 and p90r_150 p00u_150 and p00r_150 p00u_150 and p10r_150 p00u_150 and p90_600 p00u_150 and p00_600 p00u_150 and p10_600 p00u_150 and p90u_600 p00u_150 and p10u_600 p00u_150 and p90r_600 p00u_150 and p00r_600 p00u_150 and p10r_600 p00u_150 and p90_300 p00u_150 and p00_300 p00u_150 and p10_300 p00u_150 and p90u_300 p00u_150 and p00u_300 p00u_150 and p10u_300 p00u_150 and p90r_300 p00u_150 and p00r_300 p00u_150 and p10r_300 p10u_150 and p00u_600 p00r_150 and p00u_600 p10r_150 and p00u_600 p90_600 and p00u_600 p00_600 and p00u_600 p10_600 and p00u_600 p90u_600 and p00u_600 p00u_600 and p10u_600 p00u_600 and p90r_600 p00u_600 and p00r_600 p00u_600 and p10r_600 p00u_600 and p90_300 p00u_600 and p00_300 p00u_600 and p10_300 p00u_600 and p90u_300 p00u_600 and p00u_300 p00u_600 and p10u_300 p00u_600 and p90r_300 p00u_600 and p00r_300 p00u_600 and p10r_300 Latitude and Longitude Latitude and p90u_75 Latitude and p00u_75 Latitude and p10u_75 p90_1200 and p90u_75 p90_1200 and p00u_75 p90_1200 and p10u_75 p00_1200 and p90u_75 p00_1200 and p00u_75 p00_1200 and p10u_75 p10_1200 and p90u_75 p10_1200 and p00u_75 p10_1200 and p10u_75 p90u_1200 and p90u_75 p90u_1200 and p00u_75 p90u_1200 and p10u_75 p00u_1200 and p90u_75 p00u_1200 and p00u_75 p00u_1200 and p10u_75 p10u_1200 and p90u_75 p10u_1200 and p00u_75 p10u_1200 and p10u_75 p00r_1200 and p90u_75 p00r_1200 and p00u_75 p00r_1200 and p10u_75 p10r_1200 and p90u_75 p10r_1200 and p00u_75 p10r_1200 and p10u_75 p90_30 and p90u_75 p90_30 and p00u_75 p90_30 and p10u_75 p00_30 and p90u_75 p00_30 and p00u_75 p00_30 and p10u_75 p10_30 and p90u_75 p10_30 and p00u_75 p10_30 and p10u_75 p90r_30 and p90u_75 p90r_30 and p00u_75 p90r_30 and p10u_75 p00r_30 and p90u_75 p00r_30 and p00u_75 p00r_30 and p10u_75 p10r_30 and p90u_75 p10r_30 and p00u_75 p10r_30 and p10u_75 p90_75 and p90u_75 p90_75 and p00u_75 p90_75 and p10u_75 p10_75 and p90u_75 p10_75 and p00u_75 p10_75 and p10u_75 Longitude and p90_1200 Longitude and p00_1200 Longitude and p10_1200 Longitude and p90u_1200 Longitude and p00u_1200 Longitude and p10u_1200 Longitude and p00r_1200 Longitude and p10r_1200 Longitude and p90_30 Longitude and p00_30 Longitude and p10_30 Longitude and p90r_30 Longitude and p00r_30 Longitude and p10r_30 Longitude and p90_75 Longitude and p10_75 Longitude and p90r_75 Longitude and p00r_75 Longitude and p10r_75 Longitude and p90_150 Longitude and p10_150 Longitude and p90u_150 Longitude and p10u_150 Longitude and p90r_150 Longitude and p00r_150 Longitude and p10r_150 Longitude and p90_600 Longitude and p00_600 Longitude and p10_600 Longitude and p90u_600 Longitude and p10u_600 Longitude and p90r_600 Longitude and p00r_600 Longitude and p10r_600 Longitude and p90_300 Longitude and p00_300 Longitude and p10_300 Longitude and p90u_300 Longitude and p00u_300 Longitude and p10u_300 Longitude and p90r_300 Longitude and p00r_300 Longitude and p10r_300 p90u_75 and p90r_75 p90u_75 and p00r_75 p90u_75 and p10r_75 p90u_75 and p90_150 p90u_75 and p00_150 p90u_75 and p10_150 p90u_75 and p90u_150 p90u_75 and p10u_150 p90u_75 and p90r_150 p90u_75 and p00r_150 p90u_75 and p10r_150 p90u_75 and p90_600 p90u_75 and p00_600 p90u_75 and p10_600 p90u_75 and p90u_600 p90u_75 and p10u_600 p90u_75 and p90r_600 p90u_75 and p00r_600 p90u_75 and p10r_600 p90u_75 and p90_300 p90u_75 and p00_300 p90u_75 and p10_300 p90u_75 and p90u_300 p90u_75 and p00u_300 p90u_75 and p10u_300 p90u_75 and p90r_300 p90u_75 and p00r_300 p90u_75 and p10r_300 p00u_75 and p90r_75 p00u_75 and p00r_75 p00u_75 and p10r_75 p00u_75 and p90_150 p00u_75 and p00_150 p00u_75 and p10_150 p00u_75 and p90u_150 p00u_75 and p10u_150 p00u_75 and p90r_150 p00u_75 and p00r_150 p00u_75 and p10r_150 p00u_75 and p90_600 p00u_75 and p00_600 p00u_75 and p10_600 p00u_75 and p90u_600 p00u_75 and p10u_600 p00u_75 and p90r_600 p00u_75 and p00r_600 p00u_75 and p10r_600 p00u_75 and p90_300 p00u_75 and p00_300 p00u_75 and p10_300 p00u_75 and p90u_300 p00u_75 and p00u_300 p00u_75 and p10u_300 p00u_75 and p90r_300 p00u_75 and p00r_300 p00u_75 and p10r_300 p10u_75 and p90r_75 p10u_75 and p00r_75 p10u_75 and p10r_75 p10u_75 and p90_150 p10u_75 and p00_150 p10u_75 and p10_150 p10u_75 and p90u_150 p10u_75 and p10u_150 p10u_75 and p90r_150 p10u_75 and p00r_150 p10u_75 and p10r_150 p10u_75 and p90_600 p10u_75 and p00_600 p10u_75 and p10_600 p10u_75 and p90u_600 p10u_75 and p10u_600 p10u_75 and p90r_600 p10u_75 and p00r_600 p10u_75 and p10r_600 p10u_75 and p90_300 p10u_75 and p00_300 p10u_75 and p10_300 p10u_75 and p90u_300 p10u_75 and p00u_300 p10u_75 and p10u_300 p10u_75 and p90r_300 p10u_75 and p00r_300 p10u_75 and p10r_300 Longitude and p00_150 p90r_1200 and p10u_30 p90r_1200 and p00u_150 p10u_30 and p00u_600 p90r_1200 and p90u_30 p90r_1200 and p00u_30 p90r_1200 and p00_75 p90r_1200 and p00u_600 p90u_30 and p00_75 p90u_30 and p00u_150 p90u_30 and p00u_600 p00u_30 and p00_75 p00u_30 and p00u_150 p00u_30 and p00u_600 p10u_30 and p00_75 p10u_30 and p00u_150 p00_75 and p00u_150 p00_75 and p00u_600 p00u_150 and p00u_600 Longitude and p90r_1200 Longitude and p90u_30 Longitude and p00u_30 Longitude and p00u_150 p90r_1200 and p90u_75 p90r_1200 and p00u_75 p90r_1200 and p10u_75 p90u_30 and p90u_75 p90u_30 and p00u_75 p90u_30 and p10u_75 p00u_30 and p90u_75 p00u_30 and p00u_75 p00u_30 and p10u_75 p10u_30 and p90u_75 p10u_30 and p00u_75 p10u_30 and p10u_75 p90u_75 and p00u_600 p00u_75 and p00u_150 p00u_75 and p00u_600 p10u_75 and p00u_150 Longitude and p10u_30 Longitude and p00_75 Longitude and p00u_600 p90u_75 and p00u_150 p10u_75 and p00u_600 Longitude and p90u_75 Longitude and p00u_75 Longitude and p10u_75 p90r_1200 and Country_encoded p00_75 and Country_encoded p90u_30 and Country_encoded p00u_30 and Country_encoded p10u_30 and Country_encoded p00u_600 and Country_encoded p00u_150 and Country_encoded p90_1200 and Country_encoded p00u_1200 and Country_encoded p00_300 and Country_encoded p90u_300 and Country_encoded Latitude and Country_encoded p00_1200 and Country_encoded p10_1200 and Country_encoded p90u_1200 and Country_encoded p10u_1200 and Country_encoded p00r_1200 and Country_encoded p10r_1200 and Country_encoded p90_30 and Country_encoded p00_30 and Country_encoded p10_30 and Country_encoded p00r_30 and Country_encoded p10r_30 and Country_encoded p90_75 and Country_encoded p10_75 and Country_encoded p90r_75 and Country_encoded p00r_75 and Country_encoded p10r_75 and Country_encoded p90_150 and Country_encoded p00_150 and Country_encoded p10_150 and Country_encoded p90u_150 and Country_encoded p10u_150 and Country_encoded p90r_150 and Country_encoded p00r_150 and Country_encoded p10r_150 and Country_encoded p90_600 and Country_encoded p00_600 and Country_encoded p10_600 and Country_encoded p90u_600 and Country_encoded p10u_600 and Country_encoded p90r_600 and Country_encoded p00r_600 and Country_encoded p10r_600 and Country_encoded p90_300 and Country_encoded p10_300 and Country_encoded p00u_300 and Country_encoded p10u_300 and Country_encoded p90r_300 and Country_encoded p00r_300 and Country_encoded p10r_300 and Country_encoded p90r_30 and Country_encoded Dependent variables: p90u_75 and p00u_75 p90u_75 and p10u_75 p00u_75 and p10u_75
df
| Latitude | Longitude | p90_1200 | p00_1200 | p10_1200 | p90u_1200 | p00u_1200 | p10u_1200 | p90r_1200 | p00r_1200 | ... | p90_300 | p00_300 | p10_300 | p90u_300 | p00u_300 | p10u_300 | p90r_300 | p00r_300 | p10r_300 | Country_encoded | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.352765 | 0.251194 | -0.464402 | -0.508016 | -0.554848 | -0.593107 | -0.660152 | -0.734917 | -0.252291 | -0.282518 | ... | -1.172443 | -1.136296 | -1.125151 | -1.191514 | -1.198046 | -1.215751 | -0.738981 | -0.675829 | -0.636833 | 0.605681 |
| 1 | -0.126329 | -0.064588 | -0.774040 | -0.731805 | -0.679515 | -0.964721 | -0.953322 | -0.919337 | -0.444835 | -0.404765 | ... | -0.586885 | -0.600261 | -0.556108 | -0.716401 | -0.736024 | -0.708998 | -0.154835 | -0.193095 | -0.156917 | 0.514631 |
| 2 | -4.916056 | -0.579314 | -1.002907 | -0.916580 | -0.857975 | -1.183329 | -1.120005 | -1.079853 | -0.644420 | -0.576372 | ... | 0.414370 | 0.496365 | 0.605076 | 0.653952 | 0.779507 | 0.935261 | -0.156248 | -0.111894 | -0.068467 | -1.670572 |
| 3 | -0.469286 | -1.226995 | -0.889115 | -0.813688 | -0.764634 | -0.908713 | -0.841270 | -0.801030 | -0.714629 | -0.655143 | ... | -1.145335 | -1.094991 | -1.071174 | -1.169357 | -1.162423 | -1.166418 | -0.712229 | -0.638664 | -0.593155 | 1.060932 |
| 4 | -0.020197 | 0.018589 | 0.051419 | 0.029799 | 0.051246 | 0.176309 | 0.148003 | 0.179467 | -0.085040 | -0.085873 | ... | -0.741186 | -0.728155 | -0.685837 | -0.701982 | -0.714859 | -0.682794 | -0.559051 | -0.517095 | -0.473250 | 0.514631 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 271 | -1.506439 | 1.501828 | 1.792834 | 1.899641 | 1.913771 | 0.839405 | 1.051898 | 1.110167 | 2.454943 | 2.384831 | ... | 1.727114 | 2.251499 | 2.276462 | 1.196198 | 1.746700 | 1.828051 | 2.090747 | 2.335818 | 2.210780 | -1.397422 |
| 272 | 0.096301 | -0.957393 | -0.854920 | -0.820254 | -0.771264 | -0.789761 | -0.786267 | -0.743154 | -0.772925 | -0.718363 | ... | 0.245594 | 0.188036 | 0.253678 | 0.620326 | 0.569365 | 0.671899 | -0.509843 | -0.477951 | -0.437206 | 1.060932 |
| 273 | -0.461599 | 1.689799 | 2.357024 | 2.340021 | 2.270520 | 2.538004 | 2.634711 | 2.644594 | 1.762702 | 1.682191 | ... | 0.566912 | 0.570582 | 0.541617 | 0.867854 | 0.941671 | 0.931923 | -0.165667 | -0.201114 | -0.199622 | -0.395870 |
| 274 | 0.461080 | 0.470339 | 0.061055 | -0.037393 | -0.131383 | -0.141744 | -0.234249 | -0.340844 | 0.257537 | 0.153308 | ... | -0.455104 | -0.551699 | -0.640980 | -0.537094 | -0.643647 | -0.744355 | -0.153133 | -0.229645 | -0.287221 | 0.878832 |
| 275 | 0.074844 | -1.154910 | -0.616189 | -0.577300 | -0.524737 | -0.479518 | -0.452401 | -0.391292 | -0.648693 | -0.600271 | ... | -0.497452 | -0.464913 | -0.420298 | -0.360175 | -0.330085 | -0.282576 | -0.574150 | -0.530945 | -0.488370 | 1.060932 |
276 rows × 57 columns
from sklearn.decomposition import PCA
# Separate features and target variables
features = df.drop([ "p00_1200", "p10_1200", "p00u_1200", "p10u_1200", "p00r_1200", "p10r_1200", "p00_30", "p10_30", "p00u_30", "p10u_30", "p00r_30", "p10r_30", "p00_75", "p10_75", "p00u_75", "p10u_75", "p00r_75", "p10r_75", "p00_150", "p10_150", "p00u_150", "p10u_150", "p00r_150", "p10r_150", "p00_600", "p10_600", "p00u_600", "p10u_600", "p00r_600", "p10r_600", "p00_300", "p10_300", "p00u_300", "p10u_300", "p00r_300", "p10r_300"], axis=1)
target_variables = df[[ "p00_1200", "p10_1200", "p00u_1200", "p10u_1200", "p00r_1200", "p10r_1200", "p00_30", "p10_30", "p00u_30", "p10u_30", "p00r_30", "p10r_30", "p00_75", "p10_75", "p00u_75", "p10u_75", "p00r_75", "p10r_75", "p00_150", "p10_150", "p00u_150", "p10u_150", "p00r_150", "p10r_150", "p00_600", "p10_600", "p00u_600", "p10u_600", "p00r_600", "p10r_600", "p00_300", "p10_300", "p00u_300", "p10u_300", "p00r_300", "p10r_300"]]
# Apply dimensionality reduction using PCA
pca = PCA(n_components=2) # Set the desired number of components
features_reduced = pca.fit_transform(features)
# Create a new DataFrame for the reduced features
df_reduced = pd.DataFrame(features_reduced, columns=['PC1', 'PC2'])
# Concatenate the reduced features with the target variables
df_combined = pd.concat([df_reduced, target_variables], axis=1)
df_reduced
| PC1 | PC2 | |
|---|---|---|
| 0 | -2.329699 | 1.201499 |
| 1 | -2.724107 | -0.899067 |
| 2 | -0.970523 | -0.068136 |
| 3 | -3.801980 | -0.339716 |
| 4 | -1.927433 | -0.712879 |
| ... | ... | ... |
| 271 | 3.797920 | -2.980111 |
| 272 | -1.871871 | 0.237314 |
| 273 | 2.285090 | -1.486060 |
| 274 | -1.308275 | -0.415028 |
| 275 | -1.430205 | 2.411526 |
276 rows × 2 columns
target_variables
| p00_1200 | p10_1200 | p00u_1200 | p10u_1200 | p00r_1200 | p10r_1200 | p00_30 | p10_30 | p00u_30 | p10u_30 | ... | p00u_600 | p10u_600 | p00r_600 | p10r_600 | p00_300 | p10_300 | p00u_300 | p10u_300 | p00r_300 | p10r_300 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.508016 | -0.554848 | -0.660152 | -0.734917 | -0.282518 | -0.308726 | 1.307545 | 1.234529 | 1.464840 | 1.379198 | ... | -1.198856 | -1.233763 | -0.622001 | -0.599107 | -1.136296 | -1.125151 | -1.198046 | -1.215751 | -0.675829 | -0.636833 |
| 1 | -0.731805 | -0.679515 | -0.953322 | -0.919337 | -0.404765 | -0.361318 | -0.607961 | -0.582347 | -0.534148 | -0.512303 | ... | -0.931959 | -0.907064 | -0.360560 | -0.317110 | -0.600261 | -0.556108 | -0.736024 | -0.708998 | -0.193095 | -0.156917 |
| 2 | -0.916580 | -0.857975 | -1.120005 | -1.079853 | -0.576372 | -0.526636 | -0.483534 | -0.456906 | -0.410897 | -0.388234 | ... | -0.326982 | -0.245381 | -0.386586 | -0.339928 | 0.496365 | 0.605076 | 0.779507 | 0.935261 | -0.111894 | -0.068467 |
| 3 | -0.813688 | -0.764634 | -0.841270 | -0.801030 | -0.655143 | -0.609732 | -0.558129 | -0.534108 | -0.468648 | -0.447666 | ... | -1.038341 | -1.026704 | -0.628250 | -0.584963 | -1.094991 | -1.071174 | -1.162423 | -1.166418 | -0.638664 | -0.593155 |
| 4 | 0.029799 | 0.051246 | 0.148003 | 0.179467 | -0.085873 | -0.068557 | -0.600080 | -0.574276 | -0.543570 | -0.521872 | ... | -0.713870 | -0.672779 | -0.408781 | -0.367127 | -0.728155 | -0.685837 | -0.714859 | -0.682794 | -0.517095 | -0.473250 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 271 | 1.899641 | 1.913771 | 1.051898 | 1.110167 | 2.384831 | 2.304732 | -0.392303 | -0.377247 | -0.430769 | -0.413749 | ... | 0.717522 | 0.768739 | 1.553292 | 1.483419 | 2.251499 | 2.276462 | 1.746700 | 1.828051 | 2.335818 | 2.210780 |
| 272 | -0.820254 | -0.771264 | -0.786267 | -0.743154 | -0.718363 | -0.671430 | -0.549863 | -0.526056 | -0.473721 | -0.452860 | ... | -0.051293 | 0.024048 | -0.615747 | -0.572764 | 0.188036 | 0.253678 | 0.569365 | 0.671899 | -0.477951 | -0.437206 |
| 273 | 2.340021 | 2.270520 | 2.634711 | 2.644594 | 1.682191 | 1.579212 | -0.446240 | -0.434000 | -0.480364 | -0.462969 | ... | 0.077725 | 0.059601 | 0.144024 | 0.120417 | 0.570582 | 0.541617 | 0.941671 | 0.931923 | -0.201114 | -0.199622 |
| 274 | -0.037393 | -0.131383 | -0.234249 | -0.340844 | 0.153308 | 0.072083 | -0.195625 | -0.247802 | -0.062048 | -0.120870 | ... | -0.755498 | -0.870089 | -0.144182 | -0.223553 | -0.551699 | -0.640980 | -0.643647 | -0.744355 | -0.229645 | -0.287221 |
| 275 | -0.577300 | -0.524737 | -0.452401 | -0.391292 | -0.600271 | -0.556288 | 0.277647 | 0.303837 | 0.480277 | 0.499443 | ... | -0.488171 | -0.440802 | -0.578217 | -0.535978 | -0.464913 | -0.420298 | -0.330085 | -0.282576 | -0.530945 | -0.488370 |
276 rows × 36 columns
df_combined
| PC1 | PC2 | p00_1200 | p10_1200 | p00u_1200 | p10u_1200 | p00r_1200 | p10r_1200 | p00_30 | p10_30 | ... | p00u_600 | p10u_600 | p00r_600 | p10r_600 | p00_300 | p10_300 | p00u_300 | p10u_300 | p00r_300 | p10r_300 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -2.329699 | 1.201499 | -0.508016 | -0.554848 | -0.660152 | -0.734917 | -0.282518 | -0.308726 | 1.307545 | 1.234529 | ... | -1.198856 | -1.233763 | -0.622001 | -0.599107 | -1.136296 | -1.125151 | -1.198046 | -1.215751 | -0.675829 | -0.636833 |
| 1 | -2.724107 | -0.899067 | -0.731805 | -0.679515 | -0.953322 | -0.919337 | -0.404765 | -0.361318 | -0.607961 | -0.582347 | ... | -0.931959 | -0.907064 | -0.360560 | -0.317110 | -0.600261 | -0.556108 | -0.736024 | -0.708998 | -0.193095 | -0.156917 |
| 2 | -0.970523 | -0.068136 | -0.916580 | -0.857975 | -1.120005 | -1.079853 | -0.576372 | -0.526636 | -0.483534 | -0.456906 | ... | -0.326982 | -0.245381 | -0.386586 | -0.339928 | 0.496365 | 0.605076 | 0.779507 | 0.935261 | -0.111894 | -0.068467 |
| 3 | -3.801980 | -0.339716 | -0.813688 | -0.764634 | -0.841270 | -0.801030 | -0.655143 | -0.609732 | -0.558129 | -0.534108 | ... | -1.038341 | -1.026704 | -0.628250 | -0.584963 | -1.094991 | -1.071174 | -1.162423 | -1.166418 | -0.638664 | -0.593155 |
| 4 | -1.927433 | -0.712879 | 0.029799 | 0.051246 | 0.148003 | 0.179467 | -0.085873 | -0.068557 | -0.600080 | -0.574276 | ... | -0.713870 | -0.672779 | -0.408781 | -0.367127 | -0.728155 | -0.685837 | -0.714859 | -0.682794 | -0.517095 | -0.473250 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 271 | 3.797920 | -2.980111 | 1.899641 | 1.913771 | 1.051898 | 1.110167 | 2.384831 | 2.304732 | -0.392303 | -0.377247 | ... | 0.717522 | 0.768739 | 1.553292 | 1.483419 | 2.251499 | 2.276462 | 1.746700 | 1.828051 | 2.335818 | 2.210780 |
| 272 | -1.871871 | 0.237314 | -0.820254 | -0.771264 | -0.786267 | -0.743154 | -0.718363 | -0.671430 | -0.549863 | -0.526056 | ... | -0.051293 | 0.024048 | -0.615747 | -0.572764 | 0.188036 | 0.253678 | 0.569365 | 0.671899 | -0.477951 | -0.437206 |
| 273 | 2.285090 | -1.486060 | 2.340021 | 2.270520 | 2.634711 | 2.644594 | 1.682191 | 1.579212 | -0.446240 | -0.434000 | ... | 0.077725 | 0.059601 | 0.144024 | 0.120417 | 0.570582 | 0.541617 | 0.941671 | 0.931923 | -0.201114 | -0.199622 |
| 274 | -1.308275 | -0.415028 | -0.037393 | -0.131383 | -0.234249 | -0.340844 | 0.153308 | 0.072083 | -0.195625 | -0.247802 | ... | -0.755498 | -0.870089 | -0.144182 | -0.223553 | -0.551699 | -0.640980 | -0.643647 | -0.744355 | -0.229645 | -0.287221 |
| 275 | -1.430205 | 2.411526 | -0.577300 | -0.524737 | -0.452401 | -0.391292 | -0.600271 | -0.556288 | 0.277647 | 0.303837 | ... | -0.488171 | -0.440802 | -0.578217 | -0.535978 | -0.464913 | -0.420298 | -0.330085 | -0.282576 | -0.530945 | -0.488370 |
276 rows × 38 columns
# Define the number of folds for cross-validation
n_splits = 5
kf = KFold(n_splits=n_splits, shuffle=True)
# Assuming you have multiple target variables named 'target_variable1', 'target_variable2', etc.
target_variables = [
"p00_1200", "p10_1200", "p00u_1200", "p10u_1200", "p00r_1200", "p10r_1200",
"p00_30", "p10_30", "p00u_30", "p10u_30", "p00r_30", "p10r_30",
"p00_75", "p10_75", "p00u_75", "p10u_75", "p00r_75", "p10r_75",
"p00_150", "p10_150", "p00u_150", "p10u_150", "p00r_150", "p10r_150",
"p00_600", "p10_600", "p00u_600", "p10u_600", "p00r_600", "p10r_600",
"p00_300", "p10_300", "p00u_300", "p10u_300", "p00r_300", "p10r_300"
]
from sklearn.model_selection import KFold, train_test_split
from sklearn.metrics import mean_absolute_error, mean_squared_error
import numpy as np
# Define the number of folds for cross-validation
n_splits = 5
kf = KFold(n_splits=n_splits, shuffle=True)
# Define lists to store the errors for each target variable
rmse_list = []
mae_list = []
# Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(df_combined.drop(target_variables, axis=1),
df_combined[target_variables],
test_size=0.2,
random_state=42)
# Iterate over the folds
for train_index, val_index in kf.split(X_train):
# Get the training and validation sets
X_train_fold, X_val = X_train.iloc[train_index], X_train.iloc[val_index]
y_train_fold, y_val = y_train.iloc[train_index], y_train.iloc[val_index]
# Initialize the linear regression model
model = LinearRegression()
# Fit the model on the training fold
model.fit(X_train_fold, y_train_fold)
# Evaluate the model on the validation set
y_pred_val = model.predict(X_val)
# Calculate the evaluation metrics (e.g., RMSE, MAE) for each target variable
for i, target_variable in enumerate(target_variables):
rmse_val = np.sqrt(mean_squared_error(y_val[target_variable], y_pred_val[:, i]))
mae_val = mean_absolute_error(y_val[target_variable], y_pred_val[:, i])
# Append the errors to the respective lists
rmse_list.append(rmse_val)
mae_list.append(mae_val)
print("RMSE for", target_variable, ":", rmse_val)
print("MAE for", target_variable, ":", mae_val)
RMSE for p00_1200 : 0.4457543516975941 MAE for p00_1200 : 0.29375579370585503 RMSE for p10_1200 : 0.44276274340015603 MAE for p10_1200 : 0.2876463982993365 RMSE for p00u_1200 : 0.5905821192761743 MAE for p00u_1200 : 0.42262046638374956 RMSE for p10u_1200 : 0.5899532445469068 MAE for p10u_1200 : 0.423360384178612 RMSE for p00r_1200 : 0.45285046449514604 MAE for p00r_1200 : 0.29888282330871774 RMSE for p10r_1200 : 0.46225344983684646 MAE for p10r_1200 : 0.30026948359086947 RMSE for p00_30 : 0.5461759850416852 MAE for p00_30 : 0.40375187326038947 RMSE for p10_30 : 0.5485598057181358 MAE for p10_30 : 0.40255950624708403 RMSE for p00u_30 : 0.5428906114758772 MAE for p00u_30 : 0.39046360463060226 RMSE for p10u_30 : 0.5398002762467695 MAE for p10u_30 : 0.3852605880534004 RMSE for p00r_30 : 0.3757722687678801 MAE for p00r_30 : 0.3174084851521543 RMSE for p10r_30 : 0.39152423828596083 MAE for p10r_30 : 0.32456720809925477 RMSE for p00_75 : 0.37418727257302503 MAE for p00_75 : 0.27487018398128477 RMSE for p10_75 : 0.4041527736721407 MAE for p10_75 : 0.29465712038119246 RMSE for p00u_75 : 0.4220456712367099 MAE for p00u_75 : 0.28617511112033445 RMSE for p10u_75 : 0.4448706814189256 MAE for p10u_75 : 0.29937351279159324 RMSE for p00r_75 : 0.3240150673425609 MAE for p00r_75 : 0.25925900120471224 RMSE for p10r_75 : 0.3451164246431476 MAE for p10r_75 : 0.2799231129882589 RMSE for p00_150 : 0.4093815565900085 MAE for p00_150 : 0.2625136891851467 RMSE for p10_150 : 0.38382078433228034 MAE for p10_150 : 0.26511578577359374 RMSE for p00u_150 : 0.5254513417614372 MAE for p00u_150 : 0.3528038314208217 RMSE for p10u_150 : 0.49303076873385643 MAE for p10u_150 : 0.3449681854315745 RMSE for p00r_150 : 0.37698635887567983 MAE for p00r_150 : 0.29538016402474887 RMSE for p10r_150 : 0.41605310946336327 MAE for p10r_150 : 0.32275871965141933 RMSE for p00_600 : 0.30313662182257445 MAE for p00_600 : 0.25330542631144837 RMSE for p10_600 : 0.313897834437518 MAE for p10_600 : 0.2598572915207517 RMSE for p00u_600 : 0.5378941293896304 MAE for p00u_600 : 0.4185726390947062 RMSE for p10u_600 : 0.5371240930832142 MAE for p10u_600 : 0.4215951836187972 RMSE for p00r_600 : 0.37819044964446386 MAE for p00r_600 : 0.2591511791325185 RMSE for p10r_600 : 0.4386450103045798 MAE for p10r_600 : 0.28254592612339097 RMSE for p00_300 : 0.3795990867330439 MAE for p00_300 : 0.29208062678512176 RMSE for p10_300 : 0.4282262241813048 MAE for p10_300 : 0.3028930488717397 RMSE for p00u_300 : 0.47611760339259074 MAE for p00u_300 : 0.40657299722603335 RMSE for p10u_300 : 0.4695107132189922 MAE for p10u_300 : 0.39546804185033674 RMSE for p00r_300 : 0.5642481434845623 MAE for p00r_300 : 0.3173734107888052 RMSE for p10r_300 : 0.6586924181322424 MAE for p10r_300 : 0.34576783255193216 RMSE for p00_1200 : 0.451586518295311 MAE for p00_1200 : 0.31271949479221833 RMSE for p10_1200 : 0.4758801779536445 MAE for p10_1200 : 0.31830845855805195 RMSE for p00u_1200 : 0.5275557226700944 MAE for p00u_1200 : 0.4071937002757122 RMSE for p10u_1200 : 0.5265347874990199 MAE for p10u_1200 : 0.4190653737640435 RMSE for p00r_1200 : 0.5639951231903446 MAE for p00r_1200 : 0.3393889101954135 RMSE for p10r_1200 : 0.5967308783123773 MAE for p10r_1200 : 0.3501603871832731 RMSE for p00_30 : 0.8183475629494632 MAE for p00_30 : 0.3217766071484763 RMSE for p10_30 : 0.9807061680265312 MAE for p10_30 : 0.3328253291388256 RMSE for p00u_30 : 0.9183717259543731 MAE for p00u_30 : 0.3382017361041332 RMSE for p10u_30 : 1.0883941262336394 MAE for p10u_30 : 0.35100830655114845 RMSE for p00r_30 : 0.4320004444048697 MAE for p00r_30 : 0.3264534360671443 RMSE for p10r_30 : 0.42854966256959925 MAE for p10r_30 : 0.32455337657891487 RMSE for p00_75 : 0.28540900280342635 MAE for p00_75 : 0.21352524368303624 RMSE for p10_75 : 0.30503611586473817 MAE for p10_75 : 0.22694747330525067 RMSE for p00u_75 : 0.29811747177291475 MAE for p00u_75 : 0.2208891746939972 RMSE for p10u_75 : 0.31744387783393363 MAE for p10u_75 : 0.2334866387644823 RMSE for p00r_75 : 0.3679576082409595 MAE for p00r_75 : 0.30800185147463305 RMSE for p10r_75 : 0.3636148314767283 MAE for p10r_75 : 0.30218374331404463 RMSE for p00_150 : 0.4772410187077925 MAE for p00_150 : 0.3307518296656004 RMSE for p10_150 : 0.4780739428517326 MAE for p10_150 : 0.3501712689552395 RMSE for p00u_150 : 0.5756945540013212 MAE for p00u_150 : 0.3691918835655446 RMSE for p10u_150 : 0.568606832554198 MAE for p10u_150 : 0.37972964607985454 RMSE for p00r_150 : 0.3670599259220306 MAE for p00r_150 : 0.29859924942666943 RMSE for p10r_150 : 0.38621880653790824 MAE for p10r_150 : 0.3163475272360772 RMSE for p00_600 : 0.25388793143501254 MAE for p00_600 : 0.18943373662984603 RMSE for p10_600 : 0.2508133343558573 MAE for p10_600 : 0.18299955014676772 RMSE for p00u_600 : 0.49856288033692175 MAE for p00u_600 : 0.37562276075452955 RMSE for p10u_600 : 0.4911009332023029 MAE for p10u_600 : 0.3683916057085638 RMSE for p00r_600 : 0.3669646264947517 MAE for p00r_600 : 0.27225385283106124 RMSE for p10r_600 : 0.39918518554503846 MAE for p10r_600 : 0.2950150396650625 RMSE for p00_300 : 0.41521946292342926 MAE for p00_300 : 0.2883949928823283 RMSE for p10_300 : 0.4107777040483665 MAE for p10_300 : 0.31099822975832603 RMSE for p00u_300 : 0.6004619791082793 MAE for p00u_300 : 0.35507648224593263 RMSE for p10u_300 : 0.5900623993646696 MAE for p10u_300 : 0.367309819449931 RMSE for p00r_300 : 0.36383274975205887 MAE for p00r_300 : 0.2956078679617915 RMSE for p10r_300 : 0.39930024795606456 MAE for p10r_300 : 0.3263310118666914 RMSE for p00_1200 : 0.36440215463493425 MAE for p00_1200 : 0.2177928567933897 RMSE for p10_1200 : 0.3067503182183593 MAE for p10_1200 : 0.21177679034835914 RMSE for p00u_1200 : 0.8237217341932423 MAE for p00u_1200 : 0.3897958268564565 RMSE for p10u_1200 : 0.7718332008486726 MAE for p10u_1200 : 0.3762157250517775 RMSE for p00r_1200 : 0.4065300165281175 MAE for p00r_1200 : 0.25641511874407646 RMSE for p10r_1200 : 0.46385551371481487 MAE for p10r_1200 : 0.275035402816845 RMSE for p00_30 : 0.45128518207810503 MAE for p00_30 : 0.3298949527454421 RMSE for p10_30 : 0.4702196193465141 MAE for p10_30 : 0.3349176084225349 RMSE for p00u_30 : 0.43635582062027317 MAE for p00u_30 : 0.3284313569901618 RMSE for p10u_30 : 0.4305277914394484 MAE for p10u_30 : 0.33132073172475396 RMSE for p00r_30 : 1.0533634644981178 MAE for p00r_30 : 0.45673339041804495 RMSE for p10r_30 : 1.1443436364130553 MAE for p10r_30 : 0.4741882712040605 RMSE for p00_75 : 0.5897637693906156 MAE for p00_75 : 0.33257470550593776 RMSE for p10_75 : 0.6248935423143939 MAE for p10_75 : 0.34328829328560634 RMSE for p00u_75 : 0.5741922552025899 MAE for p00u_75 : 0.3036446818210753 RMSE for p10u_75 : 0.5693735535892865 MAE for p10u_75 : 0.3003072737158901 RMSE for p00r_75 : 1.1258840030691406 MAE for p00r_75 : 0.4124866038471531 RMSE for p10r_75 : 1.2287571846713354 MAE for p10r_75 : 0.433888180073809 RMSE for p00_150 : 0.5971245238290968 MAE for p00_150 : 0.3136107167820519 RMSE for p10_150 : 0.6934110577989023 MAE for p10_150 : 0.3483710567205829 RMSE for p00u_150 : 0.5170645765924193 MAE for p00u_150 : 0.3425292765871046 RMSE for p10u_150 : 0.5488039749008344 MAE for p10u_150 : 0.3642457907525939 RMSE for p00r_150 : 1.0875894843930376 MAE for p00r_150 : 0.40005892262237447 RMSE for p10r_150 : 1.1814576387015767 MAE for p10r_150 : 0.4195346659959771 RMSE for p00_600 : 0.4385492389817212 MAE for p00_600 : 0.28743023445650717 RMSE for p10_600 : 0.3701733824323431 MAE for p10_600 : 0.27652911093332594 RMSE for p00u_600 : 0.926286217361586 MAE for p00u_600 : 0.5342867718303466 RMSE for p10u_600 : 0.8894009325806502 MAE for p10u_600 : 0.5394985405796621 RMSE for p00r_600 : 0.3722980943233305 MAE for p00r_600 : 0.21302782886281477 RMSE for p10r_600 : 0.453101218482213 MAE for p10r_600 : 0.23010019092898018 RMSE for p00_300 : 0.40103156662313094 MAE for p00_300 : 0.2920824422104269 RMSE for p10_300 : 0.42930813650386596 MAE for p10_300 : 0.3230223441637964 RMSE for p00u_300 : 0.6914764449160106 MAE for p00u_300 : 0.4878822332775537 RMSE for p10u_300 : 0.6765798084121786 MAE for p10u_300 : 0.49919554441586356 RMSE for p00r_300 : 0.6922612352189481 MAE for p00r_300 : 0.2758395328017106 RMSE for p10r_300 : 0.7705590567461069 MAE for p10r_300 : 0.29938847848375655 RMSE for p00_1200 : 0.41213946472355 MAE for p00_1200 : 0.25890733081976164 RMSE for p10_1200 : 0.4460235193507126 MAE for p10_1200 : 0.29110074068962216 RMSE for p00u_1200 : 0.5271392451096022 MAE for p00u_1200 : 0.33970113105059696 RMSE for p10u_1200 : 0.5245468814017356 MAE for p10u_1200 : 0.3455057674920692 RMSE for p00r_1200 : 0.5832214015423443 MAE for p00r_1200 : 0.37798285071607984 RMSE for p10r_1200 : 0.6227313383821846 MAE for p10r_1200 : 0.3882818197515841 RMSE for p00_30 : 0.6895086669346445 MAE for p00_30 : 0.33296889431338167 RMSE for p10_30 : 0.6861170618111022 MAE for p10_30 : 0.3481785741208395 RMSE for p00u_30 : 0.7233355166301713 MAE for p00u_30 : 0.31126514954260043 RMSE for p10u_30 : 0.7086311832855555 MAE for p10u_30 : 0.32238602964118507 RMSE for p00r_30 : 0.4253862105948033 MAE for p00r_30 : 0.29513970032315234 RMSE for p10r_30 : 0.4358608215235226 MAE for p10r_30 : 0.31227313084506775 RMSE for p00_75 : 0.49374598435906997 MAE for p00_75 : 0.36248181949807656 RMSE for p10_75 : 0.5287229053396267 MAE for p10_75 : 0.3876974169369334 RMSE for p00u_75 : 0.5327777597661745 MAE for p00u_75 : 0.35934538544763384 RMSE for p10u_75 : 0.5614968827246397 MAE for p10u_75 : 0.3829331965701304 RMSE for p00r_75 : 0.4052437185639489 MAE for p00r_75 : 0.3220190911642803 RMSE for p10r_75 : 0.4233330289142836 MAE for p10r_75 : 0.3335546067236643 RMSE for p00_150 : 0.46478433598224944 MAE for p00_150 : 0.2558262145317415 RMSE for p10_150 : 0.43926198078824874 MAE for p10_150 : 0.25729793833817616 RMSE for p00u_150 : 0.5961479346336515 MAE for p00u_150 : 0.3555967149369657 RMSE for p10u_150 : 0.563224924180827 MAE for p10u_150 : 0.33733889660155464 RMSE for p00r_150 : 0.3506240311093295 MAE for p00r_150 : 0.28337493447635276 RMSE for p10r_150 : 0.37665653008599775 MAE for p10r_150 : 0.3070428561577442 RMSE for p00_600 : 0.28869173655402075 MAE for p00_600 : 0.23570490639024 RMSE for p10_600 : 0.28617819892599544 MAE for p10_600 : 0.23633520059504623 RMSE for p00u_600 : 0.5603488696228169 MAE for p00u_600 : 0.4445382575914145 RMSE for p10u_600 : 0.5449288091774919 MAE for p10u_600 : 0.4447627663239992 RMSE for p00r_600 : 0.37755214901862816 MAE for p00r_600 : 0.2620469520198733 RMSE for p10r_600 : 0.42057894988892536 MAE for p10r_600 : 0.28560113710834795 RMSE for p00_300 : 0.42921682988859416 MAE for p00_300 : 0.32930775230446235 RMSE for p10_300 : 0.42892508191988116 MAE for p10_300 : 0.33052231402709875 RMSE for p00u_300 : 0.6554316637460061 MAE for p00u_300 : 0.4854050503689868 RMSE for p10u_300 : 0.6509237715563693 MAE for p10u_300 : 0.4921102105741653 RMSE for p00r_300 : 0.34259294601746154 MAE for p00r_300 : 0.2353491802320322 RMSE for p10r_300 : 0.3842577206512241 MAE for p10r_300 : 0.2617740170869576 RMSE for p00_1200 : 0.41584315980568726 MAE for p00_1200 : 0.3318408990236335 RMSE for p10_1200 : 0.4029551713593652 MAE for p10_1200 : 0.3212951097667516 RMSE for p00u_1200 : 0.6218664730641188 MAE for p00u_1200 : 0.4880902741901632 RMSE for p10u_1200 : 0.6144804556919659 MAE for p10u_1200 : 0.47772623056338637 RMSE for p00r_1200 : 0.38673653140081216 MAE for p00r_1200 : 0.3025692377262774 RMSE for p10r_1200 : 0.3973549597417327 MAE for p10r_1200 : 0.31359495223458395 RMSE for p00_30 : 0.40987730613872453 MAE for p00_30 : 0.32011931404371735 RMSE for p10_30 : 0.39574548648857877 MAE for p10_30 : 0.3106119303711626 RMSE for p00u_30 : 0.482710892440099 MAE for p00u_30 : 0.36281525506915485 RMSE for p10u_30 : 0.4691819674515295 MAE for p10u_30 : 0.35381066811776407 RMSE for p00r_30 : 0.7214398479541294 MAE for p00r_30 : 0.45731941091083317 RMSE for p10r_30 : 0.7333816466777405 MAE for p10r_30 : 0.4639483443883741 RMSE for p00_75 : 0.5745944476450174 MAE for p00_75 : 0.33597564510760225 RMSE for p10_75 : 0.5972191535042937 MAE for p10_75 : 0.35178960188718017 RMSE for p00u_75 : 0.6409358000684935 MAE for p00u_75 : 0.33322618214612687 RMSE for p10u_75 : 0.6591748577014722 MAE for p10u_75 : 0.34755721188683997 RMSE for p00r_75 : 0.5448630193895001 MAE for p00r_75 : 0.3575392376400605 RMSE for p10r_75 : 0.5648570166190462 MAE for p10r_75 : 0.37835650907926704 RMSE for p00_150 : 0.45869671649922045 MAE for p00_150 : 0.3462979878338465 RMSE for p10_150 : 0.4647335718953466 MAE for p10_150 : 0.37224846447919363 RMSE for p00u_150 : 0.5286306604269992 MAE for p00u_150 : 0.3552946956778211 RMSE for p10u_150 : 0.5220237948401069 MAE for p10u_150 : 0.3631720223970527 RMSE for p00r_150 : 0.45671580100754144 MAE for p00r_150 : 0.3537784938201375 RMSE for p10r_150 : 0.4894227711585231 MAE for p10r_150 : 0.38626999582751426 RMSE for p00_600 : 0.4106624920754024 MAE for p00_600 : 0.2680742366117977 RMSE for p10_600 : 0.412649262333401 MAE for p10_600 : 0.2806012952135657 RMSE for p00u_600 : 0.6368972271789962 MAE for p00u_600 : 0.47230409728705075 RMSE for p10u_600 : 0.6456061215109817 MAE for p10u_600 : 0.47234399297596996 RMSE for p00r_600 : 0.4283102558448409 MAE for p00r_600 : 0.3082765855782746 RMSE for p10r_600 : 0.4472614658437132 MAE for p10r_600 : 0.3171484859958342 RMSE for p00_300 : 0.46287432960703745 MAE for p00_300 : 0.36723769196044215 RMSE for p10_300 : 0.45979217399880384 MAE for p10_300 : 0.36102906253548245 RMSE for p00u_300 : 0.6342181661883252 MAE for p00u_300 : 0.5036842451577708 RMSE for p10u_300 : 0.6276081295483007 MAE for p10u_300 : 0.4977711784672595 RMSE for p00r_300 : 0.4195853002282249 MAE for p00r_300 : 0.29608528840966164 RMSE for p10r_300 : 0.44817019760400056 MAE for p10r_300 : 0.3313963380130839
# Fit the model on the entire training set
model.fit(X_train, y_train)
# Evaluate the model on the test set
y_pred_test = model.predict(X_test)
# Define lists to store the errors for each target variable
rmse_list1 = []
mae_list1 = []
# Calculate the evaluation metrics (e.g., RMSE, MAE) for each target variable on the test set
for i, target_variable in enumerate(target_variables):
rmse_test = np.sqrt(mean_squared_error(y_test[target_variable], y_pred_test[:, i]))
mae_test = mean_absolute_error(y_test[target_variable], y_pred_test[:, i])
# Append the errors to the respective lists
rmse_list1.append(rmse_test)
mae_list1.append(mae_test)
print("Test Set - RMSE for", target_variable, ":", rmse_test)
print("Test Set - MAE for", target_variable, ":", mae_test)
Test Set - RMSE for p00_1200 : 0.5107151250923863 Test Set - MAE for p00_1200 : 0.34184327468279546 Test Set - RMSE for p10_1200 : 0.5467840011969846 Test Set - MAE for p10_1200 : 0.3613226290772591 Test Set - RMSE for p00u_1200 : 0.5861614800286751 Test Set - MAE for p00u_1200 : 0.42925906461470564 Test Set - RMSE for p10u_1200 : 0.5805695238709324 Test Set - MAE for p10u_1200 : 0.4273011429166433 Test Set - RMSE for p00r_1200 : 0.7127956917940466 Test Set - MAE for p00r_1200 : 0.42750482416095864 Test Set - RMSE for p10r_1200 : 0.7549907094093308 Test Set - MAE for p10r_1200 : 0.4357853678238665 Test Set - RMSE for p00_30 : 0.6550765891641364 Test Set - MAE for p00_30 : 0.38616000208816537 Test Set - RMSE for p10_30 : 0.6039119454930826 Test Set - MAE for p10_30 : 0.36598182212796065 Test Set - RMSE for p00u_30 : 0.7465819141472019 Test Set - MAE for p00u_30 : 0.408651712381602 Test Set - RMSE for p10u_30 : 0.6841703089039886 Test Set - MAE for p10u_30 : 0.3857125048019022 Test Set - RMSE for p00r_30 : 0.45712433395868207 Test Set - MAE for p00r_30 : 0.31626669682975134 Test Set - RMSE for p10r_30 : 0.44181029130899574 Test Set - MAE for p10r_30 : 0.3069721260549629 Test Set - RMSE for p00_75 : 0.3840329852831813 Test Set - MAE for p00_75 : 0.26556162561973645 Test Set - RMSE for p10_75 : 0.41735297737452975 Test Set - MAE for p10_75 : 0.2852058361714428 Test Set - RMSE for p00u_75 : 0.451881541785515 Test Set - MAE for p00u_75 : 0.3083283769616057 Test Set - RMSE for p10u_75 : 0.48583719596982433 Test Set - MAE for p10u_75 : 0.3236321435096023 Test Set - RMSE for p00r_75 : 0.39524851867943905 Test Set - MAE for p00r_75 : 0.2943571298175975 Test Set - RMSE for p10r_75 : 0.38473140702219616 Test Set - MAE for p10r_75 : 0.2915819436124443 Test Set - RMSE for p00_150 : 0.6007969884718334 Test Set - MAE for p00_150 : 0.38084792859215194 Test Set - RMSE for p10_150 : 0.6112425189506123 Test Set - MAE for p10_150 : 0.39332701020957306 Test Set - RMSE for p00u_150 : 0.7341836151013474 Test Set - MAE for p00u_150 : 0.4683146944912349 Test Set - RMSE for p10u_150 : 0.7570082350386992 Test Set - MAE for p10u_150 : 0.4842090567873362 Test Set - RMSE for p00r_150 : 0.3029946621289836 Test Set - MAE for p00r_150 : 0.24267911468940698 Test Set - RMSE for p10r_150 : 0.3086403591186205 Test Set - MAE for p10r_150 : 0.25464095590481584 Test Set - RMSE for p00_600 : 0.29866582361579214 Test Set - MAE for p00_600 : 0.23133649843930162 Test Set - RMSE for p10_600 : 0.33241044385649055 Test Set - MAE for p10_600 : 0.2431271855096957 Test Set - RMSE for p00u_600 : 0.5011134583803405 Test Set - MAE for p00u_600 : 0.4027808919961598 Test Set - RMSE for p10u_600 : 0.49287851012835965 Test Set - MAE for p10u_600 : 0.3989387984104336 Test Set - RMSE for p00r_600 : 0.5304071655245188 Test Set - MAE for p00r_600 : 0.3131828484645097 Test Set - RMSE for p10r_600 : 0.5795001439972172 Test Set - MAE for p10r_600 : 0.33583616172011205 Test Set - RMSE for p00_300 : 0.4017738837747629 Test Set - MAE for p00_300 : 0.3215696551224309 Test Set - RMSE for p10_300 : 0.3971167760346604 Test Set - MAE for p10_300 : 0.3244799272845561 Test Set - RMSE for p00u_300 : 0.6095628002037142 Test Set - MAE for p00u_300 : 0.4540605821823758 Test Set - RMSE for p10u_300 : 0.6064262420092933 Test Set - MAE for p10u_300 : 0.4534899383853001 Test Set - RMSE for p00r_300 : 0.35721414797560996 Test Set - MAE for p00r_300 : 0.26213375675270656 Test Set - RMSE for p10r_300 : 0.3816623589377065 Test Set - MAE for p10r_300 : 0.2892942772811491
# Calculate the mean error for each metric
mean_rmse = np.mean(rmse_list)
mean_mae = np.mean(mae_list)
print("Mean RMSE TRAIN:", mean_rmse)
print("Mean MAE TRAIN:", mean_mae)
mean_rmse1 = np.mean(rmse_list1)
mean_mae1 = np.mean(mae_list1)
print("Mean RMSE TEST:", mean_rmse1)
print("Mean MAE TEST:", mean_mae1)
Mean RMSE TRAIN: 0.5223470225200729 Mean MAE TRAIN: 0.3377257988535565 Mean RMSE TEST: 0.5167604076036582 Mean MAE TEST: 0.3504354862632292